System for determining a confidence factor for insurance underwriting suitable for use by an automated system

ABSTRACT

A system is described for evaluating the decision-making confidence of a process and system for at least a partial underwriting of insurance policies where placement of an insurance application to an underwriting category is based on its similarity to previous insurance applications. The confidence factor computed is a measure of the correctness of the decision for a given application for insurance.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority from U.S. Provisional PatentApplication Ser. No. 60/343,249, which was filed on Dec. 31, 2001.

BACKGROUND OF THE INVENTION

The present invention relates to a system for underwriting insuranceapplications, and more particularly to a system for determining aconfidence factor for underwriting insurance applications based on acomparison with previous underwritten applications.

A trained individual or individuals traditionally perform insuranceunderwriting. A given application for insurance (also referred to as an“insurance application”) may be compared against a plurality ofunderwriting standards set by an insurance company. The insuranceapplication may be classified into one of a plurality of risk categoriesavailable for a type of insurance coverage requested by an applicant.The risk categories then affect a premium paid by the applicant, e.g.,the higher the risk category, the higher the premium. A decision toaccept or reject the application for insurance may also be part of thisrisk classification, as risks above a certain tolerance level set by theinsurance company may simply be rejected.

There can be a large amount of variability in the insurance underwritingprocess when performed by individual underwriters. Typically,underwriting standards cannot cover all possible cases and variations ofan application for insurance. The underwriting standards may even beself-contradictory or ambiguous, leading to uncertain application of thestandards. The subjective judgment of the underwriter will almost alwaysplay a role in the process. Variation in factors such as underwritertraining and experience, and a multitude of other effects can causedifferent underwriters to issue different, inconsistent decisions.Sometimes these decisions can be in disagreement with the establishedunderwriting standards of the insurance company, while sometimes theycan fall into a “gray area” not explicitly covered by the underwritingstandards.

Further, there may be an occasion in which an underwriter's decisioncould still be considered correct, even if it disagrees with the writtenunderwriting standards. This situation can be caused when theunderwriter uses his/her own experience to determine whether theunderwriting standards may or should be interpreted and/or adjusted.Different underwriters may make different determinations about whenthese adjustments are allowed, as they might apply stricter or moreliberal interpretations of the underwriting standards. Thus, thejudgment of experienced underwriters may be in conflict with the desireto consistently apply the underwriting standards.

Most of the key information required for automated insuranceunderwriting is structured and standardized. However, some sources ofinformation may be non-standard or not amenable to standardization. Byway of example, an attending physician statement (“APS”) may be almostas unique as each individual physician. However, a significant fractionof applications may require the use of one or more APS due to thepresence of medical impairments, age of applicants, or other factors.Without such key information, the application underwriting processcannot be automated for these cases.

Conventional methods for dealing with some of the problems describedabove have included having human underwriters directly reading the APS.However, an APS document can be as long as several tens of pages.Therefore, the manual reading process, combined with note-taking andconsulting other information, such as an underwriting manual or thelike, can greatly extend the cycle-time for each application processed,increase underwriter variability, and limit capacity by preventing theautomation of the decision process.

Other drawbacks may also exist.

SUMMARY OF THE INVENTION

An exemplary embodiment of the present invention provides a system fordetermining the confidence factor for an insurance applicationunderwriting decision based on a comparison of at least one previousunderwritten insurance application, comprising means for identifying atleast one internal parameter that may affect the probability ofmisclassification of the insurance application, means for estimating theconditional probability of misclassification for the at least oneidentified parameter, means for translating the conditional probabilityof misclassification into a soft constraint for the at least oneparameter, and means for defining a run-time function to evaluate theconfidence threshold based on the soft constraint

An additional embodiment of the invention provides a system fordetermining the confidence factor for an application decision based on acomparison of at least one previous application decision comprisingmeans for identifying at least one internal parameter that may affectthe probability of misclassification of the application, means forestimating the conditional probability of misclassification for the atleast one identified parameter, means for translating the conditionalprobability of misclassification into a soft constraint for the at leastone parameter, and means for defining a run-time function to evaluatethe confidence threshold based on the soft constraints.

According to a further embodiment of the present invention, a system fordetermining the confidence factor for an automated insurance applicationunderwriting decision based on a comparison of at least one previousunderwritten insurance application comprises means for identifying atleast one internal parameter that may affect the probability ofmisclassification of the insurance application, and means for estimatingthe conditional probability of misclassification for the at least oneidentified parameter, including: a) means for accessing a case basecontaining a plurality of cases whose associated application decisionshave been certified correct; b) means for selecting one of the pluralityof cases, where the selected case is defined as the probe case; c) meansfor identifying at least one internal parameter of the probe case thatmay affect the probability of misclassification; d) means fordetermining the underwriting classification of the probe case based onthe remaining plurality of cases within the case base; e) means forcomparing the underwriting classification determination with theoriginal certified decision of the probe case; and f) means forrecording the comparison and the at least one identified internalparameter for the probe case. The system also comprises means fortranslating the conditional probability of misclassification into a softconstraint for the at least one parameter, and means for defining arun-time function to evaluate the confidence threshold based on the softconstraint.

By way of another exemplary embodiment of the invention, a system fordetermining the confidence factor for an automated insurance applicationunderwriting decision based on a comparison of at least one previousunderwritten insurance application comprises means for identifying atleast one internal parameter that may affect the probability ofmisclassification of the insurance application, means for estimating theconditional probability of misclassification for the at least oneidentified parameter, means for translating the conditional probabilityof misclassification into a soft constraint for the at least oneparameter, including comprising a penalty for misclassification and areward for correct classification, and means for defining a run-timefunction to evaluate the confidence threshold based on the softconstraint.

A further embodiment of the invention provides a system for determiningthe confidence factor for an automated insurance applicationunderwriting decision based on a comparison of at least one previousunderwritten insurance application. The system comprises means foridentifying at least one internal parameter that may affect theprobability of misclassification of the insurance application, means forestimating the conditional probability of misclassification for the atleast one identified parameter, means for translating the conditionalprobability of misclassification into a soft constraint for the at leastone parameter through an aggregating functions using a weighted sum ofrewards and penalties with increasing penalties for misclassifications,and means for defining a run-time function to evaluate the confidencethreshold based on the soft constraint.

Another exemplary embodiment of the invention provides a system fordetermining the confidence factor for an automated insurance applicationunderwriting decision based on a comparison of at least one previousunderwritten insurance application comprising means for identifying atleast one internal parameter that may affect the probability ofmisclassification of the insurance application, means for estimating theconditional probability of misclassification for the at least oneidentified parameter, and means for translating the conditionalprobability of misclassification into a soft constraint for the at leastone parameter. The system also comprises means for defining a run-timefunction to evaluate the confidence threshold based on the softconstraint, and means for generating a soft constraint evaluation vectorthat contains the degree to which each parameter satisfies itscorresponding soft constraint.

An additional exemplary embodiment of the invention provides a systemdetermining the confidence factor for an insurance applicationunderwriting decision based on a comparison of at least one previousunderwritten insurance application, comprising an identificationapplication for identifying at least one internal parameter that mayaffect the probability of misclassification of the insuranceapplication, an estimation module for estimating the conditionalprobability of misclassification for the at least one identifiedparameters, a translation module for translating the conditionalprobability of misclassification into a soft constraint for the at leastone parameter, and a definition module for defining a run-time functionto evaluate the confidence threshold based on the soft constraint.

In an additional exemplary embodiment of the invention, a system fordetermining the confidence factor for an application decision based on acomparison of at least one previous application decision comprises anidentification module for identifying at least one internal parameterthat may affect the probability of misclassification of the insuranceapplication, an estimation module for estimating the conditionalprobability of misclassification for the at least one identifiedparameters, a translation module for translating the conditionalprobability of misclassification into a soft constraint for the at leastone parameter, and a determination module for defining a run-timefunction to evaluate the confidence threshold based on the softconstraint.

According to a further exemplary embodiment of the invention, a systemfor determining the confidence factor for an automated insuranceapplication underwriting decision based on a comparison of at least oneprevious underwritten insurance application comprises an identificationmodule for identifying at least one internal parameter that may affectthe probability of misclassification of the insurance application, andan estimating module for estimating the conditional probability ofmisclassification for the at least one identified parameters, includingsub-components of: a) an access module for accessing a case basecontaining a plurality of cases whose associated decisions have beencertified correct; b) a selector for selecting one of the plurality ofcases, where the selected case is defined as the probe case; c) anidentification module for identifying at least one internal parameter ofthe probe case that may affect the probability of misclassification; d)a determination module for determining the underwriting classificationof the probe case based on the remaining plurality of cases within thecase base; e) a comparison module for comparing the underwritingclassification determination with the original certified decision of theprobe case; and f) a recorder for recording the comparison and the atleast one identified internal parameter for the probe case. The systemfurther comprises a translation module for translating the conditionalprobability of misclassification into a soft constraint for the at leastone parameter, and a definition module for defining a run-time functionto evaluate the confidence threshold based on the soft constraint.

Another embodiment of the present invention provides a system fordetermining the confidence factor for an automated insurance applicationunderwriting decision based on a comparison of at least one previousunderwritten insurance application comprising an identification modulefor identifying at least one internal parameter that may affect theprobability of misclassification of the insurance application, anestimation module for estimating the conditional probability ofmisclassification for the at least one identified parameter, atranslation module for translating the conditional probability ofmisclassification into a soft constraint for the at least one parameter,including a penalty for misclassification and a reward for correctclassification, and a definition module for defining a run-time functionto evaluate the confidence threshold based on the soft constraint.

In a further embodiment of the invention, a system for determining theconfidence factor for an automated insurance application underwritingdecision based on a comparison of at least one previous underwritteninsurance application comprises an identification module for identifyingat least one internal parameter that may affect the probability ofmisclassification of the insurance application, an estimation module forestimating the conditional probability of misclassification for the atleast one identified parameter, a translation module for translating theconditional probability of misclassification into a soft constraint forthe at least one parameter through an aggregating functions using aweighted sum of rewards and penalties with increasing penalties formisclassifications, and a definition module defining a run-time functionto evaluate the confidence based on the soft constraint.

In an additional exemplary embodiment of the invention, a system fordetermining the confidence factor for an automated insurance applicationunderwriting decision based on a comparison of at least one previousunderwritten insurance application is provided. The system comprises anidentification module for identifying at least one internal parameterthat may affect the probability of misclassification of the insuranceapplication, an estimation module for estimating the conditionalprobability of misclassification for the at least one identifiedparameter, a translation module for translating the conditionalprobability of misclassification into a soft constraint for the at leastone parameter, a definition module for defining a run-time function toevaluate the confidence threshold based on the soft constraint, and agenerator for generating a soft constraint evaluation vector thatcontains the degree to which the at least one parameter satisfies itscorresponding soft constraint.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a graph illustrating a fuzzy (or soft) constraint, a functiondefining for each value of the abscissa the degree of satisfaction for afuzzy rule, according to an embodiment of the invention.

FIG. 2 is a graph illustrating the measurements based on the degree ofsatisfaction for a collection of fuzzy rules, according to an embodimentof the invention.

FIG. 3 is a schematic representation of an object-oriented system todetermine the degree of satisfaction for a collection of fuzzy rules,according to an embodiment of the invention.

FIG. 4 is a flowchart illustrating steps performed in a process forunderwriting an insurance application using fuzzy logic according to anembodiment of the invention.

FIG. 5 is a flowchart illustrating steps for an inference cycleaccording to an embodiment of the invention.

FIG. 6 is a graph illustrating a fuzzy (or soft) constraint, a functiondefining for each value of the abscissa the degree of satisfaction for arule comparing similar cases, according to an embodiment of theinvention.

FIG. 7 is a graph illustrating the core of a fuzzy (or soft) constraint,according to an embodiment of the invention.

FIG. 8 is a graph illustrating the support of a fuzzy (or soft)constraint, according to an embodiment of the invention.

FIG. 9 is a graph illustrating the rate class histogram derived from aset of retrieved cases, according to an embodiment of the invention.

FIG. 10 is a chart illustrating the distribution of similarity measuresfor a set of retrieved cases, according to an embodiment of theinvention.

FIG. 11 is a table illustrating a linear aggregation of rate classes,according to an embodiment of the invention.

FIG. 12 is a flowchart illustrating the steps performed in a process fordetermining the degree of confidence of an underwriting decision basedon similar cases, according to an embodiment of the invention.

FIG. 13 is a process map illustrating a decision flow, according to anembodiment of the invention.

FIG. 14 illustrates a comparison matrix, according to an embodiment ofthe invention.

FIG. 15 illustrates a distribution of classification distances for eachbin containing a range of retrieved cases, according to an embodiment ofthe invention.

FIG. 16 illustrates a distribution of normalized percentage ofclassification distances for each bin containing a range of retrievedcases, according to an embodiment of the invention.

FIG. 17 illustrates a distribution of correct classification for eachbin containing a range of retrieved cases, according to an embodiment ofthe invention.

FIG. 18 illustrates a distribution of a performance function for eachbin containing a range of retrieved cases, according to an embodiment ofthe invention.

FIGS. 19 illustrates a distribution of a performance function for eachbin containing a range of retrieved cases, after removing negativenumbers and normalizing the values between 0 and 1, according to anembodiment of the invention.

FIG. 20 illustrates results of a plot of the preference function(derived from FIG. 19) according to an embodiment of the invention.

FIG. 21 illustrates a computation of coverage and accuracy according toan embodiment of the invention.

FIG. 22 is a schematic representation of a system for underwritingaccording to an embodiment of the invention.

FIG. 23 a flowchart illustrating the steps performed for executing andmanipulating a summarization tool according to an embodiment of theinvention.

FIG. 24 illustrates a graphic user interface for a summarization toolfor a general form according to an embodiment of the invention.

FIG. 25 illustrates a graphic user interface for a summarization toolfor a condition-specific form according to an embodiment of theinvention.

FIG. 26 illustrates an optimization process according to an embodimentof the invention.

FIG. 27 illustrates an example of an encoded population at a givengeneration according to an embodiment of the invention.

FIG. 28 illustrates a process schematic for an evaluation systemaccording to an embodiment of the invention.

FIG. 29 illustrates an example of the mechanics of an evolutionaryprocess according to an embodiment of the invention.

FIG. 30 is a graph illustrating a linear penalty function used in theevaluation of the accuracy of the CBE, according to an embodiment of theinvention.

FIG. 31 is a graph illustrating a nonlinear penalty function used in theevaluation of the accuracy of the CBE, according to an embodiment of theinvention.

DETAILED DESCRIPTION OF THE INVENTION

Reference will now be made in detail to the present preferredembodiments of the invention, examples of which are illustrated in theaccompanying drawings in which like reference characters refer tocorresponding elements.

Rules Based Reasoning

As stated above, a process and system is provided for insuranceunderwriting which is able to incorporate all of the rules in theunderwriting standards of a company, while being robust, accurate, andreliable. According to an embodiment of the invention, the process andsystem provided may be suitable for automation. Such a process andsystem may be flexible enough to adjust the underwriting standards whenappropriate. As mentioned above, each individual underwriter may havehis/her own set of interpretations of underwriting standards about whenone or more adjustments should occur. According to an embodiment of thepresent invention, rules may be incorporated while still allowing foradjustment using a fuzzy logic-based system. A fuzzy logic-based systemmay be described as a formal system of logic in which the traditionalbinary truth-values “true” and “false” are replaced by real numbers on ascale from 0 to 1. These numbers are absolute values that representintermediate truth-values for answers to questions that do not havesimple true or false, or yes or no answers. In standard binary logic, agiven rule is either satisfied (with a degree of satisfaction of 1), ornot (with a degree of satisfaction of 0), creating a sharp boundarybetween the two possible degrees of satisfaction. With fuzzy logic, agiven rule may be assigned a “partial degree of satisfaction”, a numberbetween 1 and 0, in some boundary region between a “definite yes”, and a“definite no” for the satisfaction of a given rule. Each rule will becomposed by a conjunction of conditions. Each condition will berepresented by a fuzzy set A(x), which can be interpreted as a degree ofpreference induced by a value x for satisfying a condition A. Aninference engine determines a degree of satisfaction of each conditionand an overall degree of satisfaction of a given rule.

For the purposes of illustration, imagine that a hypothetical lifeinsurance company has a plurality of risk categories, which areidentified as “cat1”, “cat2”, “cat3”, and “cat4.” In this example, arating of cat1 is a best or low risk, while cat4 is considered a worstor high risk. An applicant for an insurance policy would be rejected ifhe/she fails to be placed in any category. An example of a type of rulelaid out in a set of underwriting guidelines could be, “The applicantmay not be in cat1 if his/her cholesterol value is higher than X1.”Similarly, a cholesterol value of X2 could be a cutoff for cat2, and soon. However, it is possible that a cholesterol reading of one point overX1 may not in practice disqualify the applicant from the cat1 rating, ifall of the other rules are satisfied for cat1. It may be that readingsof one point over X1 are still allowable, and so on. To define a fuzzyrule, two parameters, X1 a and X1 b may be needed. When the applicant'scholesterol is below X1 a, a fuzzy rule may be fully satisfied (e.g., adegree of satisfaction of 1). By way of present example, X1 from theabove may be used as X1 a. A parameter X1 b may be a cutoff above whichthe fuzzy rule is fully unsatisfied (e.g., a degree of satisfaction of0). For example, it may be determined from experienced underwriters ofthe insurance company that under no circumstances can the applicant getthe cat1 rating if his/her cholesterol is above 190 (X1) by more thanfour points. In that situation, the fuzzy rule may use X1 a=X1, that is190, and X1 b=X1 a+4, that is 194. Other settings may be used. X1 a andX1 b are parameters of the model. To obtain the partial degree ofsatisfaction when the cholesterol value falls within the range [X1 a, X1b], a continuous switching function may be used, which interpolatesbetween the values 1 and 0. The simplest such function is a straightline, as disclosed in FIG. 1, but other forms of interpolation may alsobe used.

Turning to cat2, cat3, and cat4, there may be a different cholesterolrule for each category, which states that the applicant may not beplaced in that category if his/her cholesterol is higher than X2, X3, orX4, respectively. The same procedures may be used, turning each ruleinto a fuzzy logic rule by assigning high and low cutoff values (e.g.,X2 a, X2 b; X3 a, X3 b; X4 a, X4 b). Thus, by way of continuing theexample, cat2 may be associated with a fuzzy rule that uses X2 a=X2 andX2 b=X2+4, where X2=195 (for cat2). In addition X3 a=X3 and X3 b=X3+4,where X3=200 (for cat3), and X4 a=X4 and X4 b=X4, where X4=205 (forcat4). Other parameters also may be used. Similarly, one would proceedthrough each rule in the underwriting guidelines, allowing for fuzzypartial degrees of satisfaction. In the present invention, each piece ofdata may be judged many times on the basis of each rule.

Once each fuzzy rule in the rule set has been applied, a decision ismade to which category the applicant belongs. For each risk category,there may be a subset of rules that apply to that category. In order tojudge whether the applicant is eligible for the given category, somenumber of aggregation criteria may be applied. To be concrete, using theabove hypothetical case, take the subset of all rules that apply tocat1. There will be a fuzzy degree of satisfaction for every rule, wherethe set of degrees of satisfaction is called {DS−cat1}. According to anembodiment of the invention, if any of the degrees of satisfaction arezero, then the applicant may be ruled out of cat1. Thus, one of theaggregation criteria may be, “reject from cat1 if MIN({DS−cat1})<=A1,”where A1 is a chosen constant, and the notation MIN( . . . ) denotesselection of the smallest value out of the set. One choice for A1 may be0.5, but other choices may be used. By way of another example, thechoice, A1=0.7 may also be used. Again, the constant A1 may beconsidered as a parameter of the model, which may be determined.

As another aggregation rule, by way of example, if very many of therules have partial degrees of satisfaction of 0.9, then too muchadjusting may be occurring, and the applicant may be ruled out of cat1,even though the aggregation rule, MIN({DS−cat1})<=A1, may not besatisfied. The missing score (MS) is determined from the degree ofsatisfaction (DS) by MS=1−DS. If a given fuzzy rule has DS=0.9, then itwould have a missing score of 0.1. The aggregation criterion for thiscase might take the form, “reject from cat1 if SUM({MS−cat1})>=A2,”where A2 is a different chosen constant, the notation, SUM( . . . )denotes summation of all the elements of the set, and {MS−cat1} is theset of “missing scores” for each rule. The aggregation criteria abovemay use the sum of all of the missing scores for the cat1 rules as ameasure to determine when too much adjusting has been done, comparingthat with the constant A2. The measure defined above (SUM{MS−cat1}) maybe interpreted as a measure proportional to the difference between thedegree of complete satisfaction of all rules and the average degree ofsatisfaction of each rule (DS−cat1). It is understood in this inventionthat there may be any number of different kinds of aggregation criteria,of which the above two are only specific examples.

In a further step, the results of applying the aggregation criteria tothe set of rules relating to each category may be compared. A resultaccording to one example may be that the applicant is ruled out of cat1and cat2, but not from cat3 or cat4. In that case, assuming that theinsurance company's policy was to place applicants in the best possiblerisk category, the final decision would be to place the applicant incat3. Other results may also be obtained.

As stated above, this fuzzy logic system may have many parameters thatmay be freely chosen. It should be noted that the fuzzy logic system mayextend and therefore subsume a conventional (Boolean) logic system. Bysetting the fuzzy logic system parameters to have only crisp thresholds(in which the core value is equal to the support) the Boolean rules maybe represented as a case of fuzzy rules. Those parameters may be fit toreproduce a given set of decisions, or set by management in order toachieve certain results. By way of one example, a large set of cases maybe provided by the insurance company as a standard to be reproduced asclosely as possible. Preferably in such an example, there may be manycases, thereby minimizing the error between the fuzzy rules model andthe supplied cases. Optimization techniques such as logistic regression,genetic algorithms, Monte Carlo, etc., also may be used to find anoptimal set of parameters. By way of another example, some of the fuzzyrules may be determined directly by the management of the insurancecompany. This may be done through knowledge engineering sessions withexperienced underwriters, by actuaries acting on statistical informationrelated to the risk being insured or by other manners. In fact, whenconsidering maintenance of the system, initial parameters may be chosenusing optimization versus a set of cases, while at a future time, asactuarial knowledge changes, these facts may be used to directly adjustthe parameters of the fuzzy rules. New fuzzy rules may be added, oraggregation rules may change. The fuzzy logic system can be keptcurrent, allowing the insurance company to implement changes quickly andwith zero variability, thereby providing a process and system that isflexible.

According to one embodiment of the invention, the fuzzy logic parametersmay be entered into a spreadsheet to evaluate the fuzzy rules for onecase at a time. This may be essentially equivalent to implementation ina manual processing type environment. FIG. 2 is a graphicalrepresentation illustrating a plurality of measurements based on adegree of satisfaction for a rule. A graphical user interface (GUI) 200displays the degree of satisfaction for one or more rules. GUI 200includes a standard toolbar 202, which may enable a user to manipulatethe information in known manners (e.g., printing, cutting, copying,pasting, etc.). According to an embodiment of the invention, GUI may bepresented over a network using a browser application such as InternetExplorer®, Netscape Navigator®, etc. An address bar 204 may enable theuser to indicate what portion is displayed. A chart 206 displays variousinsurance decision components and how each insurance decision componentsatisfies its associated rule. A plurality of columns 208 illustrates aplurality of categories for each decision component, as well as aplurality of parameters for each decision component. A column 210identifies the actual parameters of the potential applicant forinsurance and a plurality of columns 212 illustrate a degree ofsatisfaction of each rule. By way of example, a row 214 is labeled BP(Sys), corresponding to a systolic blood pressure rule. To receive theBest or Preferred category classification, the applicant must have asystolic blood pressure score (score) between 140 and 150. To receive aSelect category classification, the applicant must have a score between150 and 155, while a score of 155 or more receives a “Standard Plus” orSt. Plus category classification. In this example, the applicant has ascore of 151. The columns 212 show zero satisfaction of the rule for theBest and Preferred category classifications. Additionally, FIG. 2 showsthat the applicant slightly missed satisfaction for the Select category,and Perfect Constraint Satisfaction for the St. Plus Category.

In another example, a row 216 is labeled BP (Dia.), corresponding to adiastolic blood pressure rule. To receive a Best categoryclassification, the applicant must have a diastolic blood pressure score(score) between 85 and 90, between 90 and 95 for a Preferred categoryclassification, between 90 and 95 for the Select categoryclassification, and between 95 and 100 for the St. Plus categoryclassification. Here, the applicant has a score of 70, resulting inPerfect Constraint Satisfaction in all of the columns 212.

By way of a further example, a row 218 is labeled Nicotine, where ascore between 4 and 5 receives the Best category classification, a scorebetween 2.5 and 3 receives the Preferred category classification, ascore between 1.5 and 2 receives the Select category classification, anda score between 0.7 and 1 receives the St. Plus category classification.In this example, the applicant has a score of 4.2. Thus, a score of“Mostly Missing” is indicated under the Best category of a column 212,while a score of Perfect Constraint Satisfaction is indicated for allothers.

GUI 200 presents a submit button 220 to enable the user to accept adecision and submit it to a database. Alternatively, the user may decidenot to accept the decision. The user may activate a next button 222 torecord his/her decision. Other methods for display may also be used.

According to another embodiment of the invention, the rules may beencoded into a Java-based computer code, which can query a database toobtain the case parameters, and write its decision in the database aswell. The object model of the java implementation is illustrated in FIG.3. This java implementation may be suitable for batch processing, or foruse in a fully automated underwriting environment. According to anembodiment of the invention, a rule engine (class RuleEngine) 302 may bethe control of the system. The decision components of rule engine 302may be composed of several rules (class Rule) 304, several aggregations(class Aggregation) 306 and zero or one decision post-processors (classDecisionPostProcessor) 308. A Rule object 304 may represent the fuzzylogic for one or a group of variables. Each rule is further composed ofa number of rateclasses (class Rateclass) 310. A Rateclass object 310defines the rules for a specific rateclass. According to an embodimentof the invention, a Rateclass object 310 may comprise two parts. Thefirst is pre-processing (class Preprocessor) 312, which may processmultiple inputs to form one output. The second is post-processing (classPostprocessor) 314, which may take the result of the pre-processing,feed it to a fuzzy function and get a fuzzy score. Some of the rules maybe conditional, such as the variable blood pressure systolic, where thethresholds vary depending on the age of the applicant. Class Condition316 may represent such a condition, if there is any. Classes FixedScore318, Minimal and Maximal may define some special preprocessingfunctions, and class Linear 320 may define the general linear fuzzyfunction as illustrated in FIG. 1.

According to an embodiment of the invention, there may be two phases atruntime for rule engine 302. The first phase may be initialization. Inthe process, the rule definition file in XML format configures the ruleengine. All the rule engine parameters are defined in the process, forexample, number of rules, the fuzzy thresholds, pre and post processingand aggregation operation (including class Intersection 322 and SumMissing 324) and class ThresholdLevel 326. The second phase may bescoring. After correct initialization, the fireEngine method in ruleengine 302 may take an input parameter—an instance of class Case 328containing all the required variable values, and output an instance ofclass Result 330, which encapsulates all the decision results, includingrateclass placement, the fuzzy scores for each variable and eachrateclass, and the aggregation scores. Class ResultLogger 332 may logthe output. Other object models for a java implementation may also beused.

FIG. 4 is a flowchart illustrating the steps performed in a process forunderwriting an insurance application using fuzzy logic rules accordingto an embodiment of the invention. At step 400, a request to underwritean insurance application may be received. The request to underwrite maycome directly from a consumer (e.g., the person being insured), aninsurance agent or another person. The request to underwrite comprisesinformation about one or more components of the insurance application.According to an embodiment of the invention, the components may includethe various characteristics associated with the individual to beinsured, such as a cholesterol level, a blood pressure level, a pulse,and other characteristics.

At step 410, at least one decision component is evaluated. As describedabove, evaluating a decision component may comprise evaluating adecision component using a fuzzy logic rule. To perform the evaluation,a rule may be defined and assigned to the decision component. While eachrule is generally only assigned one decision component, it is understoodthat more than one decision component may be assigned to each rule.Further, parameters for each rule may be defined, as also describedabove.

At step 420, at least one measurement is assigned to the at least onedecision component. As described above with regard to the application ofa fuzzy logic rule, a measurement may be assigned to the decisioncomponent from a sliding scale, such as between zero (0) and one (1).Other types of measurements may also be assigned.

At step 430, each decision component is assigned a specific componentcategory based on the assigned measurement. As described above, a numberof specific component categories are defined. Based on the assignedmeasurements, each decision component is assigned to one or morespecific component categories. By way of the examples above, thespecific component categories may be defined as cat1, cat2, cat3, andcat4. Cat1 may only be assigned decision components at a certain levelor higher. Similarly, cat2 may only be assigned decision components at asecond level or higher and so on. Other methods for assigning a specificcomponent category may also be used.

At step 440, the insurance application is assigned to a category.According to an embodiment of the invention, the categories to which theinsurance application is assigned are the same as the categories towhich the insurance decision components are assigned. As describedabove, the insurance application may be assigned to a category basedupon how the decision components were assigned. Thus, by way of example,an insurance application may be assigned to cat1 only if two or fewerdecision components are assigned to cat2 and all other decisioncomponents are assigned to cat1. Other methods for assigning aninsurance application to a category may also be used.

At step 450, an insurance policy is issued. Based on the category towhich it is assigned, certain amounts are paid to maintain the insurancepolicy in a manner that is well known in the industry. It is understoodthat based on a category, an insurance policy may not be issued. Thecustomers may decide the premiums are too high. Alternatively, theinsurance company may determine that the risk is too great, and decidenot to issue the insurance policy.

Case Based Reasoning

A rule-based reasoning (RBR) system may provide for an underwritingprocess by following a generative approach, typically a rule-chainingapproach, in which a deductive path is created from the evidence (facts)to the decisions (goals). A case-based reasoning (CBR) system, on theother hand, may follow an analogical approach rather than a deductiveapproach. In such a system, a reasoner may determine the correct rateclass suitable for underwriting by noticing a similarity of anapplication for insurance with one or more previously underwritteninsurance applications and by adapting known solutions of suchpreviously underwritten insurance applications instead of developing asolution from scratch. A plurality of underwriting descriptions andtheir solutions are stored in a CBR Case Base and are the basis formeasurement of the CBR performance. According to an embodiment of theinvention, a CBR system may be only as good as the cases within its CaseBase (also referred to as “CB”) and its ability to retrieve the mostrelevant cases in response to a new situation.

A case-based reasoning system can provide an alternative to arules-based expert system, and may be especially appropriate when anumber of rules needed to capture an expert's knowledge is unmanageable,when a domain theory is too weak or incomplete, or when such domaintheory is too dynamic. The CBR system has been successful in areas whereindividual cases or precedents govern the decision-making processes.

In many aspects, a case-based reasoning system and process is a problemsolving method different from other artificial intelligence approaches.In particular, instead of using only general domain dependent heuristicknowledge, such as in the case of an expert system, specific knowledgeof concrete, previously experienced, problem situations may be used withCBR. Another important characteristic may be that CBR impliesincremental learning, as a new experience is memorized and available forfuture problem solving each time a problem is solved. CBR may involvesolving new problems by identifying and adapting solutions to similarproblems stored in a library of past experiences.

According to an embodiment of the invention, an inference cycle of theCBR process may comprise a plurality of steps, as illustrated in theflow chart of FIG. 5. At step 502, probing and retrieving one or morerelevant cases from a case library is performed. Ranking the retrievedrelevant cases, based on a similarity measure occurs at step 504. Atstep 506, one or more best cases are selected. At step 508, one or moreretrieved relevant cases are adapted to a current case. The retrieved,relevant cases are evaluated versus the current case, based on aconfidence factor at step 510. The newly solved case is stored in thecase memory at step 512.

These steps will be illustrated below within the context of insuranceunderwriting. However, one of ordinary skill in the art will recognizethat these steps may be used in other contexts as well. For purposes ofthis example only, assume that an applicant provides his/her vital signinformation (e.g., an age, a weight, a height, a systolic blood pressurelevel and a diastolic blood pressure level, a cholesterol level and aratio, etc.) as a vector equal to:X=[x₁,x₂, x_(n)].

Furthermore, in this example, assume that two of the valuescorresponding to the cholesterol level, and a weight-to-height ratio,are above normal levels, while the others fall within normal ranges. Thefirst two components of vector X correspond to the cholesterol level(x₁) and the weight-to-height ratio (x₂). For purposes of this example,the applicant has an abnormally high cholesterol ratio (8.5%) and isover-weight (weight-to-height ratio=3.8 lb/inch). Furthermore, theapplicant has one medical condition/history, for instance a history ofhypertension. This condition may require the applicant to provideadditional detailed information related to the history of hypertension,e.g., a cardiomegaly, a chest pain, a blood pressure mean and a trendover the past three months (where mean is the average of the bloodpressure readings over a particular time period and trend corresponds tothe slope of the reading such as upward, or downward, etc.) The detailedinformation may be contained in a vector Y=[y₁, y₂, . . . , y_(p)],where the value of p will vary according to the applicant's medicalcondition.

The first step in the CBR methodology may be to represent a new case(probe) as a query in a structured query language (SQL), which may beformulated against a database of previously placed applicants (cases).According to an embodiment of the invention, the SQL query may be of theform:Q: [f ₁(x), f ₂(x), . . . ,f _(n)(x)] AND [Condition=label]where [f₁(x), f₂(x), . . . ,f_(n)(x)], will be a vector of n fuzzypreference functions, one of each of the elements of vector X, and alabel will be an index representing the applicant's current medicalcondition. For this example, the CBR system may retrieve all previousapplicants with a history of hypertension, whose vital signs werenormal, except for a cholesterol ratio and a weight-to-height ratio. Inother words, the SQL query may be for all cases matching the samecondition and similar vital information as the applicant. An example ofsuch a SQL query may be:Q1=[Support(Around (8.5%;x)), Support (Around (3.8;x)), Support(Normal(i)), . . . , Support (Normal(n))] AND [Condition=Hypertension]

The meaning of Normal(i) may be determined by a fuzzy logic setrepresenting a soft threshold for a variable, x(i), as it is used in thestricter class rate, (e.g., Preferred Best in the case of LifeInsurance.) FIG. 6 illustrates the case of Normal (j), where x(j)corresponds to the cholesterol ratio. For example, it may be determinedfrom the most experienced underwriters of the insurance company thatunder no circumstances can the applicant get the best class rate ifhis/her cholesterol ratio is above X1 by more than five points. In thatexample, one may use X1 b−X1 a=5. The specific values for X1 a and X1 bmay be parameters of the model, and will be explained below in greaterdetail. To obtain the partial degree of satisfaction when thecholesterol ratio value falls within the range [X1 a, X1 b], acontinuous switching function may be used which interpolates between thevalues 1 and 0. The simplest such function is a straight line, but otherfunctions may also be used.

In a linear membership function as shown in FIG. 6, the values X1 a andX1 b are the low and high cutoffs, respectively. A strict yes/no rulemay be recovered in the limit that X1 a=X1 b. Thus, many methods thatmix fuzzy and strict rules in any proportion may be covered as a subsetof this method.

Around (a; x) may be determined by a fuzzy relationship, whosemembership function can be interpreted as the degree to which the valuex meets the property of “being around a.” If Around (a; x)=1, then thevalue of x may be close to a well within a desired tolerance. Thesupport of the fuzzy relationship Around (a; x) may be defined as theinterval of values of x for which Around (a; x)>0, as illustrated inFIG. 7. If Around (a; x)=0 then the value of x is too far from a, beyondany acceptable tolerance.

The core of the fuzzy relationship Around (a; x) may be defined as theinterval of values of x for which Around (a; x)=1, as illustrated inFIG. 8. Any value belonging to the core fully satisfies the propertyand, in terms of a preference, it is indistinguishable from any othervalue in the core.

A trapezoidal membership distribution representing the relationship mayhave a natural preference interpretation. The support of thedistribution may represent a range of tolerable values and correspond toan interval-value used in an initial SQL retrieval query. The core mayrepresent the most desirable range of values and may establish a toppreference. By definition, a feature value falling inside the core willreceive a preference value of 1. As the feature value moves away from amost desirable range, its associated preference value will decrease from1 to 0. By retrieving the cases having cholesterol ratios falling in thesupport of Around (8.5%; x) and having weight-to-height ratios fallingin the support of Around (3.8; x) all possible relevant cases may beretrieved.

In executing an SQL query Q1 of the above example against the CBRdatabase, N cases may be retrieved. By construction, all N cases musthave all of their vital values inside the support of the correspondingelement x(i) defined by Q1. Furthermore, all cases must be related tothe same medical condition, (e.g., hypertension).

At this point, considering the outputs of each of the N retrieved casesmay provide a first preliminary decision. According to an embodiment ofthe invention, a decision may be made only on the retrieved cases, i.e.,only using the first n variables and the label used in the SQL query Q1.Each retrieved case may be referred to as a case C_(k) (k between 1 andN), and an output classification of case C_(k) as O_(k), where O_(k) isa variable having an attribute value indicating the rate class assignedto the applicant corresponding to case C_(k). By way of example, O_(k)may assume one out of T possible values, i.e., O_(k)=L, where L ε{R₁,R₂, . . . ,R_(T)}. For instance, in the case of Life insurance products,L={Preferred-Best, Preferred, Preferred-Nicotine, . . . , Standard, . .. , Table-32}. Other values may also be used.

In this example, the SQL query Q1 retrieves 40 cases (N=40). FIG. 9illustrates the histogram (distribution of the retrieved cases over therate classes) of the results of the SQL query Q1. As seen in FIG. 9, afirst preliminary decision indicates Table-II as being the most likelyrate class for the new applicant represented by the SQL query Q1.

All N cases may have all their vital values inside the support of thecorresponding element x(i) defined by the SQL query Q1 and they are allrelated to the same medical condition, (e.g., hypertension). Therefore,each case may also contain p additional elements corresponding to thevariables specific to the medical condition. A case C_(k) (k between 1and N) may be represented as an r-dimensional vector, where r=n+p. Thefirst n elements correspond to the n vital sign described by the vectorX, namely [x_(1,k), x_(2,k), . . . ,x_(n,k)]. The remaining p elementsmay correspond to the specific features related to the conditionhypertension, namely [x_((n+1),k), x_((n+2),k), . . . , x_(r,k)]. Thevalue of p may vary according to the value of the label, i.e., themedical condition.

A degree of matching between case C_(k) and the SQL query Q1 may bedetermined. To this extent, the n-dimensional vector M(C_(k), Q1) may bedefined as an evaluation of each of the functions [f₁(x), f₂(x), . . .,f_(n)(x)] from the SQL Query Q1 with the first n elements of C_(k),namely [x_(1,k), x_(2,k), . . . , x_(n,k)]:M(C _(k) , Q1)=[f ₁(x _(1,k)), f ₂(x _(2,k)), . . . , f _(n)(x _(n,k))]At the end of this evaluation, each case will have a preference vectorwhose elements take values in the (0,1] interval (where the notation(0,1] indicates that this is an open interval at 0 (i.e., it does notinclude the value 0), and a closed interval at 1 (i.e., it includes thevalue 1)). These values may represent a partial degree of membership ofthe feature value in each case and the fuzzy relationships representingpreference criteria in the SQL query Q1. Since this preference vectorrepresents a partial order, the CBR system aggregates its elements togenerate a ranking of the case, according to their overall preference.

A determination is made of an n-dimensional weight vector W=[w₁, w₂, . .. , w_(n)] in which the element w_(i) takes a value in the interval[0,1] and determines the relative importance of feature i in M(C_(k),Q1), i.e., the relevance of f_(i) (x_(i,k)). According to an embodimentof the invention, this can be done via direct elicitation from anunderwriter or using pair-wise comparisons, following Saaty's method. Byway of example, if all features are equally important, all theircorresponding weights may be equal to 1. Other methods may also be used.Once the weight vector has been determined, several aggregatingfunctions are used to rank the cases, where the aggregating functionwill map an n-dimensional unitary hypercube into a one-dimensional unitinterval, i.e.,: [0,1]^(n)−>[0,1].

To consider compensation among the elements, a definition is made of theaggregating function A[W,M(C_(k), Q1)] as a weighted sum of itselements, i.e.:

${A\left\lbrack {W,{M\left( {C_{k},{Q\; 1}} \right)}} \right\rbrack} = {\sum\limits_{i = 1}^{n}{w_{i}{f_{i}\left( x_{i,k} \right)}}}$

Alternatively, a strict intersection aggregation without compensationmay be obtained using a weighted minimum, i.e.:A[W,M(C _(k) , Q1)]=Minimum_(1, n)[max(1−w _(i)),f(x _(i,k))]Regardless of the aggregating function selected, it may be considered asa measure of similarity between the each retrieved case C_(k) and thequery Q1, and may be referred to as S(k,1). Using this measure, casesmay be sorted according to an overall degree of preference, which may beinterpreted as a measure of similarity between each retrieved case C_(k)and the query Q1.

In the first preliminary decision, the output of case C_(k) may bereferred to as O_(k), where O_(k) is a variable whose attribute valueindicates a rate class assigned to the applicant corresponding to a caseC_(k). Assume, for example, that O_(k) can take one out of T possiblevalues, i.e., O_(k)=L, where L ε {R₁, R₂, . . . ,R_(T)}. For instance,in the case of Life insurance products, L={Preferred-Best, Preferred,Preferred-Nicotine, . . . , Standard, . . . , Table-32}. However, notall cases are equally similar to our probe. FIG. 10 illustrates adistribution of the similarity measure S(k,1) over the T for theretrieved N cases (e.g., N=40 in the present example).

According to an embodiment of the invention, a minimum similarity valuemay be considered for a case. For instance, to only consider similarcases, a threshold may be established on the similarity value. By way ofexample, only cases with a similarity greater or equal to 0.5 may beconsidered. According to an embodiment of the invention, a determinationmay be made of a fuzzy cardinality of each of the rate classes, byadding up the similarity values in each class. Other distributions mayalso be evaluated.

A histogram may be drawn that aggregates the original retrievalfrequency with the similarity of the retrieved cases, and may bereferred to as a pseudo-histogram. This process may be similar to aN-Nearest Neighbor approach, where the N retrieved cases represent the Npoints in the neighborhood, and the value of S(k,1) represents thecomplement of the distance between the point K and the probe, i.e., thesimilarity between each case and a query. The rate class Ri, with thelargest cumulative measure may be proposed as a solution. By way ofexample, Table-II is the solution indicated by either option.

A decision may be made on how many cases will be used to refine asolution. Having sorted the cases along the first n dimensions, theremaining p dimensions may be analyzed corresponding to the featuresrelated to the specific medical condition. Some of these medicalconditions may have variables with binary or attribute values (e.g.,chest pain (Y/N), malignant hypertension (N), Mild, Treated, etc.),while others ones may have continuous values (e.g., cardiomegaly (% ofenlargement), systolic and diastolic blood pressure averaged and trendin past 3 months, 24 months, etc.).

An attribute-value and a binary-value may be used to select, among the Nretrieved cases, the cases that have the same values. This may be thesame as performing a second SQL query, thereby refining the first SQLquery Q1. From the originally retrieved N cases, the cases with thecorrect binary or attribute values may be selected. This may be done forall of the attribute-values and the binary-valued variables, or for asubset of the most important variables. After this selection, theoriginal set of cases will likely have been reduced. However, when aCase Base is not sufficiently large, a reduction in the number ofvariables used to perform this selection may be needed. Assuming thatthere are now L cases (where L<N), these cases may still be sortedaccording to a value of a similarity metric S(k,1).

A third preliminary decision may be obtained by re-computing thedistribution of the similarity measure S(k,1) over the T values for theoutput O_(k), and then proposing as a solution the class Ri with thelargest cumulative measure using the same pseudo-histogram methoddescribed above.

A similarity measure over the numerical features related to the medicalcondition may be obtained by establishing a fuzzy relationship Around(a;x) similar to the one described above. This fuzzy relationship wouldestablish a neighborhood of cases with similar condition intensities. Byperforming an evaluation and an aggregation similar to one describedabove, a similarity measure may be obtained by medical condition, andmay be referred to as I(k,1).

A final decision may involve creating a linear combination of bothsimilarity measures:F(k,1)=αS(k,1)+(1−α)I(k,1),thereby providing the distribution of the final similarity measureF(k,1) over the T values of O_(k). According to an embodiment of theinvention, the final decision or solution may be the class R_(i) withthe largest cumulative measure using the same pseudo-histogram method.

A reliability of the solution may be measured in several ways, and as afunction of many internal parameters computed during this process.According to an embodiment of the invention, the number of retrieved (N)and refined (L) cases (e.g., area of the histogram) may be measured.Larger values of N+L may imply a higher reliability of the solution.According to another embodiment of the invention, the fuzzy cardinalityof the retrieved and refined cases (i.e., area of the pseudo-histogram)may be measured. Larger values may imply a higher reliability of thesolution. According to a further embodiment of the invention, the shapeof the pseudo-histogram of the values of O_(k), (i.e., spread of thehistogram) may be measured, where a tighter distribution (smallersigmas) would be more reliable than scattered ones. According to anotherembodiment of the invention, the mode of the pseudo-histogram of thevalues of O_(k), (e.g., maximum value of the histogram) may be measured.Higher values of the mode may be more reliable than lower ones. Acontribution of one or more of these measurements may be used todetermine reliability. Other measurements may also be used.

Using a training set, a conditional probability of misclassification asa function of each of the above parameters may be determined, as well.Then, the (fuzzy) ranges of those parameters may be determined and aconfidence factor may be computed.

If the solution does not pass a confidence threshold (e.g., because itdoes not have enough retrieved cases, has a scattered pseudo-histogram,etc.), then the CBR system may suggest a solution to the individualunderwriter and delegate to him/her the final decision. Alternatively,if the confidence factor is above the confidence threshold, then the CBRsystem may validate the underwriter's decision. Regardless of thedecision maker, once the decision is made, the new case and itscorresponding solution are stored in the Case Base, becoming availablefor new queries.

According to an embodiment of the invention, clean cases (previouslyplaced by rule base) may be used to tune the CBR parameters (e.g.,membership functions, weights, and similarity metrics), thereby abatingrisk. Other methods for abating risk may also be used.

By defining and using three stages of preliminary decisions, the CBRsystem may display tests, thereby generating useful information for theunderwriter while the Case Base is still under development. As moreinformation (cases and variables describing each case) is stored in theCase Base, the CBR system may be able to use a more specific decisionstage.

According to an embodiment of the invention, the first two preliminarydecision stages may only require the same vital information used forclean applications and the symbolic (i.e., label) information of themedical condition. A third decision stage may make use of a subset ofthe variables describing the medical condition thereby refining the mostsimilar cases. The subset of variables may be chosen by an expertunderwriter as a function of their relevance to the insured risk(mortality, morbidity, etc.). This step will allow the CBR system torefine the set of N retrieved cases, and select the most similar Lcases, on the basis of the most important binary and attribute variablesdescribing the medical condition. The final two preliminary decisionstages may only require the same vital information used for cleanapplications and the symbolic (i.e., label) information of the medicalcondition.

According to an embodiment of the invention, it may be important that atall times the value of N (for the first two decision stages) and thevalue of L (for the third decision stage) be large enough to ensuresignificance. The number of cases used may be one of the parameters usedto compute the confidence factor described above.

In the first step of the example, the new case (probe) was representedas a SQL query, and it was assumed that only one medical condition waspresent. The complete SQL query Q may have been formulated as:Q:[f ₁(x), f ₂(x), . . . ,f _(n)(x)]AND[Condition=label]AND[Conditionnumber=1]If the applicant has more than one medical condition, the applicant maybe compared with other applicants having the same medical conditions. Byway of another example extending the original example used, theapplicant is assumed to have an abnormally high cholesterol ratio (8.5%)and be over-weight (weight-to-height ratio=3.8 lb/inch). Furthermore,the applicant discloses that he/she has two medical conditions, (e.g.,hypertension and diabetes).

In a densely populated Case Base, the applicant may be represented bythe query:Q:[f ₁(x), f ₂(x), . . . ,f _(n)(x)]AND[Condition 1=label]AND[Condition2=label 2]AND[Condition number=2]This query may be instantiated as:Q1:[Support(Around(8.5%,x)), Support(Around (3.8;x)),Support(Normal(i)), . . . , Support(Normal(n))]AND[Condition=Hypertension]AND[Condition=Diabetes]AND[Conditionnumber=2]With a well-populated Case Base, this may be a process for handlingmultiple medical conditions in complex cases.

As more conditions are added to a query, fewer cases will likely beretrieved. If the retrieved number of cases N is not significant, auseful decision may not be produced. An alternative (surrogate) solutionmay be to decompose a query into two separate queries, treating eachmedical condition separately. For instance, assuming that the modifiedquery Q1 requesting two simultaneous conditions does not yield anymeaningful result, the CBR system may decompose the query Q1 into aplurality of queries, Q1-A and Q1-B:where Q1-A:[Support(Around(8.5%,x)), Support (Around(3.8;x)), Support(Normal(i)), . . . ,Support(Normal(n))]AND[Condition=Hypertension]AND[Condition number=1];andwhere Q1-B:[Support(Around(8.5%,x)), Support (Around(3.8;x)), Support(Normal(i)), . . . ,Support(Normal(n))]AND[Condition=Diabetes]AND[Condition number=1]Each query may be treated separately and may obtain a decision on therate class for each of the queries. In other words, it may be assumedthat there are two applicants, both overweight and with a highcholesterol ratio, one with hypertension and one with diabetes.

After obtaining suggested placements in the appropriate rate class,(e.g., RC-A and RC-B, respectively) the answers may be combinedaccording to a set of aggregation rules representing the union ofmultiple rate classes induced by the presence of multiple medicalconditions. According to an embodiment of the invention, these rules maybe elicited from experienced underwriters. A look-up table, asillustrated in FIG. 11, may represent this rule set. FIG. 11 is just anexample that shows a linear aggregation of the rate classes. Assume thatthe rate class assigned to query Q1-A is RC-A=Table 6 and the rate classassigned to query Q1-B is RC-B=Table 8. The combined rate classgenerated from the aggregation rule is RC=Table 14. Other tables may bedesigned to over-penalize the occurrence of multiple conditions as theirpresence might affect risk and, therefore, claims, in a non-linearfashion. For example RC-A=Table 6 and RC-B=Table 8 could be aggregatedinto RC=Table 18 by a stricter table. Other aggregation process may alsobe used.

Additionally, these tables may be used in an associative fashion. Inother words, when an applicant has three or more medical conditions, theCBR system may aggregate the rate classes derived from the first twomedical conditions, obtain the result and aggregate the result with therate class obtained from the third medical condition, and so on, asillustrated in FIG. 11. This method is a surrogate alternative that maybe used when enough cases with multiple conditions are included in theCase Base.

According to an embodiment of the invention, a CBR engine may be encodedinto a Java based computer code, which can query a database to obtainthe case parameters, and write its decision in the database as well.This embodiment may be suitable for batch processing, and for use in afully automated underwriting environment.

Calculation of Confidence Factor

A described above, CBR may be used to automate decisions in a variety ofcircumstances, such as, but not limited to, business, commercial, andmanufacturing processes. Specifically, it may provide a method andsystem to determine at run-time a degree of confidence associated withthe output of a Case Based Decision Engine, also referred to as CBE.Such a confidence measure may enable a determination to be made on whena CBE decision is trustworthy enough to automate its execution and whenthe CBE decision is not as reliable and may need further consideration.If a CBE decision is not determined to be as reliable, a CBE analysismay still be beneficial by providing an indicator, forwarding it to ahuman decision maker, and improving the human decision maker'sproductivity with an initial screening that may limit the complexity ofthe final decision. The run-time assessment of the confidence measuremay enable the routing mechanism and increases the usefulness of a CBE.

An embodiment of the invention may comprise two parts: a) the run-timecomputation of a confidence factor for a query; and b) the determinationof the threshold to be used with the computed confidence factor. FIG. 12is a flowchart illustrating a process for determining a run-timecomputation of a confidence factor according to an embodiment of theinvention. At Step 1200, a confidence factor process is initiated. AtStep 1210, CBE internal parameters that may affect the probability ofmisclassification are identified. At Step 1220, the conditionalprobability of misclassification for each of the identified parametersis estimated. At Step 1230, the conditional probability ofmisclassification is translated into a soft constraint for eachparameter. At Step 1240, a run-time function to evaluate the confidencefactor for each new query is defined. The determination of the thresholdfor the confidence factor may be obtained by using a gradient-basedsearch. It is understood that other steps may be performed within thisprocess, and/or the order of steps may be changed. The process of FIG.12 will now be described in greater detail below.

According to an embodiment of the invention, CBE may be used to automatethe underwriting process of insurance policies. By way of example, CBEmay be used for underwriting life insurance applications, as illustratedbelow. It is understood, however, that the applicability of thisinvention is much broader, as it may apply to any Case-Based DecisionEngine(s).

According to an embodiment of the invention, an advantage of the presentinvention may include improving deployment of a method and system ofautomated insurance underwriting, based on the analysis of previoussimilar cases, as it may allow for an incremental deployment of the CBE,instead of postponing deployment until an entire case base has beencompletely populated. Further, a determination may be made for whichapplications (e.g., characterized by specific medical conditions) theCBE can provide sufficiently high confidence in the output to shift itsuse from a human underwriter productivity tool to an automated placementtool. As a case base (also referred to as a “CB”) is augmented and/orupdated by new resolved applications, the quality of the retrieved casesmay improve. Another advantage of the present invention may be that thequality of the case base may be monitored, thereby indicating theportion of the case base that requires growth or scrubbing. Forinstance, monitoring may enable identification of regions in the CB withinsufficient coverage (small area histograms, low similarity levels),regions containing inconsistent decisions (bimodal histograms), andambiguous regions (very broad histograms).

In addition, by establishing a confidence threshold, a determination maybe made whether the output can be used directly to place the applicationor if it will be a suggestion to be revised by the human underwriter,where such a determination may be made for each application processed bythe CBE. Further, according to an embodiment of the invention, a processmay be used after the deployment of the CBE, as part of maintenance ofthe case base. As the case base is enriched by the influx of new cases,the distribution of its cases may also vary. Regions of the case basethat were sparsely populated might now contain a larger number of cases.Therefore, as part of the tuning of the CBE, one may periodicallyrecompute certain steps within the process to update the softconstraints on each of the parameters. As part of the same maintenance,one may also periodically update the value of the best threshold to beused in the process.

While the present invention is described in relation to applicability tothe improvement of the performance of a Case Based Engine for DigitalUnderwriting, it is understood that the method and system describedherein may be applied to any Case Based Reasoning system, to annotatethe quality of its output and decide whether or not to act upon thegenerated output. By way of example, CBR systems may have applicationsin manufacturing, scheduling, design, diagnosis, planning, and otherareas.

As described above, the CBE relies on having a densely populated CaseBase (“CB”) from which to retrieve the precedents for the newapplication (i.e., the similar cases). According to an embodiment of theinvention, until the CB contains a sufficiently large number of casesfor most possible applications, the CBE output may not be reliable. Suchan output may, by way of example, be used as a productivity aid for ahuman underwriter, rather than an automation tool.

For each processed application, a measure of confidence in the CBEoutput is computed so that a final decision maker (CBE or humanunderwriter) may be identified. As the decision engine generates itsoutput from the retrieval, selection, and adaptation of the most similarcases, such a confidence measure may reflect the quality of the matchbetween the input (the application under consideration) and the currentknowledge, e.g., the cases used by the CBE for its decision.

The confidence measure proposed by this invention needs to reflect thequality of the match between the current application under considerationand the cases used for the CBE decision. This measure needs to beevaluated within the context of the statistics for misclassificationgathered from the training set. More specifically, according to anembodiment of the invention, the steps described below may be performed.These steps may include, but are not limited to, the following: 1)Formulate a query against the CB, reflecting the characteristics of thenew application as query constraints; 2) Retrieve the most relevantcases from the case library. For purposes of illustration, assume that Ncases have been retrieved, where N is greater than 0 (i.e., not a nullquery or an empty retrieved set of cases). A histogram of the N cases isgenerated over the universe of their responses, i.e., a frequency of therate class; 3) Rank the retrieved cases using a similarity measure; 4)Select the best cases thereby reducing the total number of usefulretrieved cases from N to L; and 5) Adapt the L refined solutions to thecurrent case in order to derive a solution for the case. By way ofexample, selecting the mode of the histogram may be used to derive asolution.

To determine the confidence in the decision, it may be desirable tounderstand what the probability of generating a correct or incorrectclassification is. Specifically, it may be desirable to identify whichfactors affect misclassifications, and, for a given case, use thesefactors to assess if it is more or less likely to generate a wrongdecision. According to an embodiment of the invention, unless a decisionis binary, the decision will consist of placing the case underconsiderations in one of several bins. Hence, there may be differentdegrees of misclassification, depending on the distance of the CBEdecision from the correct value. Given the different costs associatedwith different degrees of misclassification, the factors impacting thedecision may be used with the likely degree of misclassification.

One aspect of the present invention deals with the process and methodused to accomplish this result. At Step 1210 the CBE internal parametersthat might affect the probability of misclassification may bedetermined. Each of these parameters may be referred to as an x.Furthermore, assume that there are M parameters (i.e., i=1, . . . M,forming a parameter vector X=[x₁, x₂, . . . x_(M)].

Parameters that may affect the probability of misclassification include,but are not limited to, the following potential list of candidates:

-   -   x₁: N=Number of retrieved cases (i.e., cardinality of retrieved        set and area of histogram in FIG. 9), e.g., N=40 cases.    -   x₂: variability of retrieved cases (measure of dispersion of        histogram in FIG. 9).    -   x₃: number of retrieved cases thresholded by similarity value        (area of histogram in FIG. 10) e.g., 25 cases.    -   x₄: variability of retrieved cases thresholded by similarity        value. (measure of dispersion of histogram in FIG. 10).    -   x₅: L=number of refined cases. (i.e., cardinality of refined        set) e.g., 21 cases.    -   x₆: variability of refined cases.    -   x₇: number of refined cases, thresholded by similarity value        e.g., 16 cases.    -   x₈: variability of refined cases thresholded by similarity        value.    -   x₉: measure of strength of mode (percentage of cases in mode of        histogram) e.g., 50%.

According to an embodiment of the invention, other parameters mayinclude:

-   -   x₁₀: number of retrieved cases weighted by similarities. (i.e.        fuzzy cardinality of retrieved set (area of histogram in FIG.        9)).    -   x₁₁: variability of retrieved cases weighted by similarities        (measure of dispersion of histogram in FIG. 9).    -   x₁₂: number of refined cases weighted by similarities (i.e.        fuzzy cardinality of refined set).    -   x₁₃: variability of refined cases weighted for similarities.

These parameters may be query-dependent, (e.g., they may vary for eachnew application). This may be in contrast to static design parameters,such as, but not limited to, similarity weights, retrieval parameters,and confidence threshold. Static parameters may be tuned at developmenttime (e.g., when a system is initially developed) and periodicallyrevised at maintenance time(s) (e.g., during maintenance periods for asystem). According to an embodiment of the invention, static parametersmay be considered fixed while evaluating parameters [x₁−x₉:].

According to an embodiment of the invention, the above parameters maylikely be positively correlated. By way of example, the number orrefined cases L may depend on the total number of cases N. The relativeimpact of these parameters may be evaluated via a statisticalcorrelation analysis, CART, C4.5 or other algorithms to identify andeliminate those parameters that contribute the least amount ofadditional information. By way of another example, methods may be usedto handle partially redundant information in a way that avoids doublecounting of the evidence. The use of a minimum operator in thecomputation of the Confidence Factor, as is described below, is such anexample.

According to an embodiment of the invention, at step 1220, theconditional probability of misclassification for each parameter x_(i)(for i=1 . . . 9) may be estimated. By way of example, this step may beachieved by running a set of experiments with a training set. Given acertified Case Base (e.g., a CB containing a number K of cases whoseassociated decisions were certified correct), the following steps maythen be followed:

(1) For each of the K cases in the CB, one case is selected (from theCB) and may be considered as the probe, i.e., the case whose decision wewant to determine (1310).

(2) The Case Based Engine (CBE) and the (K−1) cases remaining in the CBmay then be used to determine the rate class (i.e., the placementdecision for the probe) (1320).

(3) The decision derived from the CBE may then be compared with theoriginal certified decision of the probe (1330).

(4) The comparison and its associated parameters [x₁−x₉] may then berecorded.

(5) The selected case may be placed in the CB and another case selected.(i.e., back to step (1) (1340)).

(6) Perform steps (2) through (5) until all the K cases in the CB havebeen used as probes (1350).

This process is illustrated in FIG. 13. Once the process is completed,the results may be collected and analyzed. The comparison matrix of FIG.14 illustrates a comparison between a probe's decision derived from theCBE and the probe's certified reference decision. The cells located onthe comparison matrix's main diagonal may contain the percentage ofcorrect classifications. The cells off the main diagonal may contain thepercentage of incorrect classifications. As was previously mentioned,there may be different degrees of misclassification, depending on thedistance of a CBE decision from the corresponding reference decision.

At this point, it may be desirable to estimate the conditionalprobability of misclassification given each of parameters [x₁−x₉]. Sinceeach case in the comparison matrix has its associated parameters [x₁−x₉]recorded, a histogram of the distance from the correct decision for eachof these parameters may be generated. This process may be illustrated bya simple example. As was previously described, the value of the firstparameter x₁:

x₁: N=Number of retrieved cases. (i.e., cardinality of retrieved set(area of histogram in FIG. 9))

FIG. 15 shows an example of cross-tabulation of classification distancesand number of retrieved cases for each probe. By way of this example,the processing of 573 probes is shown, achieving a correctclassification for 242 of them. Additionally, 214 were classified as onerate class off (where 114 at (−1) and 100 at (+1) equal 214). Further,99 were two rate classes off (where 64 at (−2) and 35 at (+2) equal 99),and 18 were 3 or more classes off. These 573 cases may also besubdivided in ten bins, representing ranges of the number of retrievedcases used for each probe. By way of example, 41 cases had between 1 and4 retrieved cases (first column), while 58 cases used more than 40retrieved cases (last column). FIG. 16 illustrates the samecross-tabulation using percentages instead of the number of cases.According to an embodiment of the invention, this table may be referredto as matrix D(i, j), where i=1 . . . 7 (the seven distancesconsidered), and j=1 . . . 10 (the ten bins considered).

Note that this table contains the same percentages illustrated in FIG.15, once we normalize the values by the total number of cases, tabulatedfor different values of x₁. For instance, the total percentage ofCorrect Classifications (CC) in FIG. 14 may be defined as the sum of theelements on the main diagonal, i.e.:

${\%\mspace{20mu}{CC}} = {\sum\limits_{i = 1}^{T}{M\left( {i,i} \right)}}$

The same percentage may be obtained by adding the percentagesdistributed along the fourth row (corresponding to Distance 0), i.e.:

${\%\mspace{20mu}{CC}} = {\sum\limits_{j = 1}^{10}{D\left( {4,j} \right)}}$

The percentage of correct classification may increase with the number ofcases retrieved for each probe (fourth row, distance=0). By analyzing agiven column on this table, an estimate may be derived of theprobability of correct/incorrect classification, given that the numberof cases is in the range of values corresponding to the column.

According to an embodiment of the invention, step 1230 may comprisetranslating the conditional probability of misclassification into a softconstraint for each parameter x₁(for i=1 . . . 9). By way of example,all misclassifications are determined to be equally undesirable, theonly concern may be with the row corresponding to distance equal 0(i.e., correct classification), as illustrated in FIG. 17. By way ofanother example, it may be desirable to penalize more thosemisclassifications that are two or three rate classes away from thecorrect decision. Therefore, an overall performance function may beformulated that aggregates the rewards of correct classifications withincreasing penalties for misclassifications. Although various types ofaggregating function may be used to achieve these ends, one possibleaggregating function may use a weighted sum of rewards and penalties.Specifically, for each bin (range of values) of the parameter x₁ underconsideration, a reward/penalty w_(i) may be considered. For instance:

${f\left( {Bin}_{k} \right)} = {\sum\limits_{i = 1}^{7}{w_{i}{D\left( {i,k} \right)}}}$

Where, for example, the weight vector W[w_(i)], i=1 . . . 7 is W=[−11,−6, −1, 4, −1, −6. −11]

This weight vector indicates that misclassifying a decision by three ormore rate classes is eleven times worse than a misclassification that isone rate class away. Except for the fourth element, which indicates thereward for correct classifications, all other elements in vector Windicate the penalty value for the corresponding degree ofmisclassification. FIG. 18 illustrates the result of applying theperformance function f(Bin_(k)) to the values of FIG. 16, i.e., MatrixD.

By interpreting the values of FIG. 18 as degree of preference, a fuzzymembership function Ci(x₁), is derived, indicating the tolerable anddesirable ranges for each parameter x₁. According to an embodiment ofthe invention, a possible way to convert the values of FIG. 18 to afuzzy membership function is to replace any negative value with a zeroand then normalize the elements by the largest value. In this example,the result of this process is illustrated in FIGS. 19 and 20.

As previously described, the membership function of a fuzzy set is amapping from the universe of discourse (the range of values of theperformance function) into the interval [0,1]. The membership functionhas a natural preference interpretation. The support of the membershipfunction Ci(x₁) represents the range of tolerable (i.e., acceptable)values of x₁. The support of the fuzzy set Ci(x₁) is defined as theinterval of values of x for which Ci(x_(i))>0. Similarly, the core mayrepresent the most desirable range of values and establish a toppreference. The core of the membership function Ci(x_(i)) may be definedas the interval of values x₁, for which Ci(x₁)=1. In the example of FIG.20, the support is [22, infinity] and the core is [40, infinity]. Bydefinition, a feature value falling inside the core will receive apreference value of 1. As the feature value moves away from the mostdesirable range, its associated preference value will decrease from 1 to0. At this point, the information may be translated into a softconstraint representing our preference for the values of parameter x₁.The soft constraint may be referred to as Ci(x₁), as illustrated in FIG.20.

According to an embodiment of the invention, a fourth step of thisinvention may be to define a run-time function to evaluate theconfidence measure for each new query. By way of example, afterexecuting the third step for each of the nine parameters, nine softconstraints may be obtained Ci(x_(i)) i=1, . . . ,9. A soft constraintevaluation (SCE) vector is generated that contains the degree to whicheach parameter satisfies its corresponding soft constraint; SCE [C₁(x₁),. . . , C₉ (x₉)]. The Confidence Factor (CF_(j)) to be associated toeach new case j may be computed at run-time as the intersection of allthe soft constraints evaluations contained in the SCE vector.

${CF}_{j} = {{\underset{i = 1}{\bigcap\limits^{9}}{C_{i}\left( x_{i} \right)}} = {\overset{9}{\underset{i = 1}{Min}}{C_{i}\left( x_{i} \right)}}}$

According to an embodiment of the invention, all elements in the SoftConstraint Evaluation (SCE) vector may be real numbers in the interval[0,1]. Therefore the Confidence Factor CF_(j) will also be a real numberin the interval [0,1]. Nine potential soft constraints represent themost desirable fuzzy ranges for the nine parameters described above.Given a new probe, its computed parameter vector X=[x₁−x₉] may used beto determine the degree to which all soft constraints are satisfied(SCE), leading to the computation of its Confidence Factor CF.

As previously described above, a four-step process was described tocompute at run-time the confidence factor. The minimum threshold for theconfidence value may be determined by a series of experiments with thedata, to avoid being too restrictive or too inclusive. Ahigher-than-needed threshold may decrease the coverage provided by theCBE by rejecting too many correct solutions (False Negatives). As thethreshold is lowered, the number of accepted solutions is increased andtherefore, an increase in coverage is obtained. However, a lower-thanneeded threshold may decrease the accuracy provided by the CBE byaccepting too many incorrect solutions (False Positives). Therefore, itmay be desirable to obtain a threshold using a method that balancesthese two concepts.

According to an embodiment of the invention, coverage for any giventhreshold level r may include accepting n(r) cases out of K. Given aCase Base with K cases, the function g₁(t) may be defined as a measureor coverage:g ₁(τ)=n(τ)/K

For accuracy, the performance function f, as previously defined, may beused (e.g., aggregate the rewards of correct classifications with theincreasing penalties for misclassifications) and may be adapted to theentire Case Base to evaluate its accuracy for any given threshold r. Asthe value of r is modified, more decisions may be accepted or rejected,modifying the entries of the comparison matrix M=[M(i,j)].

${g_{2}(\tau)} = {{\sum\limits_{i = 1}^{T}{K*R*{M\left( {i,i} \right)}}} + {\sum\limits_{i = 1}^{T}{\sum\limits_{{j = 1},{j \neq i}}^{T}{{p\left( {i,j} \right)}*R*{M\left( {i,j} \right)}}}}}$

Specifically, the function g₂(τ) may be defined as a measure of relativeaccuracy, where M(i, j) is the (i, j) element of the comparison matrixillustrated in FIG. 14. It may represent the percentage of casesclassified in cell i while the correct classification was cell j.Therefore (i=j) implies a correct classification. The percentage may becomputed over the total cases for which the decision has been accepted(i.e., its corresponding confidence was above the threshold). Further,K*R may be a reward for correct classification (where K indicates astatic multiple of basic reward R), and p(i,j)*R may be the penalty forincorrect classification (p(i,j) determine a dynamic multiple of basicreward R).

For simplicity, R=1 may be used. The penalty function p(i,j) mayindicate the increasing penalty for misclassifications further away fromthe correct one. Many possible versions of function p(i,j) can be used.By way of example, the vector W=[−11, −6, −1, 4, −1, −6, −11]corresponds to the values:K=4 andp(i,j)=5|i−j|+4

A linear penalty function p(i,j) is illustrated in FIG. 30. It will berecognized by those of ordinary skill in the art that other linearfunctions may also be used. If over-penalization for largermisclassifications is desired, a non-linear penalty function may beused, such as p(i,j)=−3(i−j)²+4, such as that illustrated in FIG. 31.

The selection of a penalty function may be left as a choice to a user torepresent the cost of different misclassifications. According to anembodiment of the invention, if there were no differences among suchcosts, then a simplified version of g₂(r) could be used to measure theCBE accuracy, e.g.:

${g_{2}(\tau)} = {\sum\limits_{i = 1}^{T}{K*R*{M\left( {i,i} \right)}}}$

Functions g₁(t) and g₂(t) may be defined to measure coverage andrelative accuracy, respectively. Function g₁(t) may be a monotonicallynon-increasing with the value t (larger values of t will not increasecoverage), while g₂(t) may be a monotonically non-decreasing with thevalue t (larger values of t will not decrease relative accuracy, unlessthe set is empty.). The two functions may be aggregated into a globalaccuracy function A(t) to evaluate the overall system performance underdifferent thresholds t:A(τ)=g ₁(τ)×g ₂(τ)where × indicates scalar multiplication

The function A(t) provides a measure of accuracy combined with thecoverage of cases. FIG. 21 illustrates an example of the computation ofCoverage, Relative Accuracy, and Global Accuracy as a function ofthreshold t. In this example, t=0.1 has the largest coverage, t=0.7 hasthe largest relative accuracy, and t=0.5 has the largest globalaccuracy.

There are many approaches that may be used to maximize the aggregatefunction A(t) to obtain the best value for threshold t. Any reasonableoptimization algorithm (such as a gradient-based search, or a combinedgradient and binary search) may be used to this effect. For example, inFIG. 21, the value of A(t) may be computed for nine values of t.According to an embodiment of the invention, values may be explored todetermine a best threshold, By way of example only, the neighborhood oft=0.5 may be explored, such as by a gradient method, to determine thatthe value t=0.55 is the best threshold.

As described above, the present invention provides many advantages.According to an embodiment of the present invention, incrementaldeployment of the CBE may be achieved, instead of postponing itsdeployment until an entire Case Base has been completely populated.Further, a determination may be made for which applications (e.g.,characterized by specific medical conditions) the CBE can providesufficiently high confidence in the output to shift its use from a humanunderwriter productivity tool to an automated placement tool.

According to an embodiment of the invention, as the Case Base isaugmented and or updated by new resolved applications, the quality ofthe retrieved cases may change. The present invention may enablemonitoring of the quality of the Case Base, indicating the part of theCB requiring growth or scrubbing. By way of example, regions within theCase Base with insufficient coverage (small area histograms, lowsimilarity levels) may be identified, as well as regions containinginconsistent decisions (bimodal histograms), and ambiguous regions (verybroad histograms).

According to an embodiment of the invention, by establishing aconfidence threshold, a determination can be made, for each applicationprocessed by the CBE, if the output can be used directly to place theapplication or if it will be a suggestion to be revised by a humanunderwriter.

According to an embodiment of the invention, a process as describedabove may be used after the deployment of the CBE, as part of the CaseBase maintenance. As the Case Based is enriched by the influx of newcases, the distribution of its cases may also vary. Regions of the CBthat were sparsely populated might now contain a larger number of cases.Therefore, as part of the tuning of the CBE, one should periodicallyrecompute various steps within the process to update the softconstraints on each of the parameters. As part of the same maintenance,the value of the best threshold may also be updated and used in theprocess.

Network-Based Underwriting System

FIG. 22 illustrates a system 2200 according to an embodiment of thepresent invention. The system 2200 comprises a plurality of computerdevices 2205 (or “computers”) used by a plurality of users to connect toa network 2202 through a plurality of connection providers (CPs) 2210.The network 2202 may be any network that permits multiple computers toconnect and interact. According to an embodiment of the invention, thenetwork 2202 may be comprised of a dedicated line to connect theplurality of the users, such as the Internet, an intranet, a local areanetwork (LAN), a wide area network (WAN), a wireless network, or othertype of network. Each of the CPs 2210 may be a provider that connectsthe users to the network 2202. For example, the CP 2210 may be anInternet service provider (ISP), a dial-up access means, such as amodem, or other manner of connecting to the network 2202. In actualpractice, there may be significantly more users connected to the system2200 than shown in FIG. 22. This would mean that there would beadditional users who are connected through the same CPs 2210 shown orthrough another CP 2210. Nevertheless, for purposes of illustration, thediscussion will presume three computer devices 2205 are connected to thenetwork 2202 through two CPs 2210.

According to an embodiment of the invention, the computer devices 2205a-2205 c may each make use of any device (e.g., a computer, a wirelesstelephone, a personal digital assistant, etc.) capable of accessing thenetwork 2202 through the CP 2210. Alternatively, some or all of thecomputer devices 2205 a-2205 c may access the network 2202 through adirect connection, such as a T1 line, or similar connection. FIG. 22shows the three computer devices 2205 a-2205 c, each having a connectionto the network 2202 through the CP 2210 a and the CP 2210 b. Thecomputer devices 2205 a-2205 c may each make use of a personal computersuch as a computer located in a user's home, or may use other deviceswhich allow the user to access and interact with others on the network2202. A central controller module 2212 may also have a connection to thenetwork 2202 as described above. The central controller module 2212 maycommunicate with one or more modules, such as one or more data storagemodules 2236, one or more evaluation modules 2224, one or more casedatabase modules 2240 or other modules discussed in greater detailbelow.

Each of the computer devices 2205 a-2205 c used may contain a processormodule 2204, a display module 2208, and a user interface module 2206.Each of the computer devices 2205 a-2205 c may have at least one userinterface module 2206 for interacting and controlling the computer. Theuser interface module 2206 may be comprised of one or more of akeyboard, a joystick, a touchpad, a mouse, a scanner or any similardevice or combination of devices. Each of the computers 2205 a-2205 cmay also include a display module 2208, such as a CRT display or otherdevice. According to an embodiment of the invention, a developer, a userof a production system, and/or a change management module may use acomputer device 2205.

The central controller module 2212 may maintain a connection to thenetwork 2202 such as through a transmitter module 2214 and a receivermodule 2216. The transmitter module 2214 and the receiver module 2216may be comprised of conventional devices that enable the centralcontroller module 2212 to interact with the network 2202. According toan embodiment of the invention, the transmitter module 2214 and thereceiver module 2216 may be integral with the central controller module2212. According to another embodiment of the invention, the transmittermodule 2214 and the receiver module 2216 may be portions of oneconnection device. The connection to the network 2202 by the centralcontroller module 2212 and the computer devices 2205 may be a highspeed, large bandwidth connection, such as through a T1 or a T3 line, acable connection, a telephone line connection, a DSL connection, oranother similar type of connection. The central controller module 2212functions to permit the computer devices 2205 a-2205 c to interact witheach other in connection with various applications, messaging servicesand other services which may be provided through the system 2200.

The central controller module 2212 preferably comprises either a singleserver computer or a plurality of server computers configured to appearto the computer devices 2205 a-2205 c as a single resource. The centralcontroller module 2212 communicates with a number of modules. Eachmodule will now be described in greater detail.

A processor module 2218 may be responsible for carrying out processingwithin the system 2200. According to an embodiment of the invention, theprocessor module 2218 may handle high-level processing, and may comprisea math co-processor or other processing devices.

A decision component category module 2220 and an application categorymodule 2222 may handle categories for various insurance policies anddecision components. As described above, each decision component andeach application may be assigned a category. The decision componentcategory module 2220 may include information related to the categoryassigned for each decision component, including a cross-reference to theapplication associated with each decision component, the assignedcategory or categories, and/or other information. The applicationcategory module 2222 may include information related to the categoryassigned for each application, including a cross-reference to thedecision components associated with each application, the assignedcategory or categories, and/or other information.

An evaluation module 2224 may include an evaluation of a decisioncomponent using one or more rules, where the rules may be fuzzy logicrules. The evaluation module 2224 may direct the application of one ormore fuzzy logic rules to one or more decision components. Further, theevaluation module 2224 may direct the application of one or more fuzzylogic rules to one or more policies within a case database 2240, to bedescribed in greater detail below. Evaluation module policies within acase database 2240, are to be described in greater detail below.

A measurement module 2226 may include measurements assigned to one ormore decision components. As described above, a measurement may beassigned to each decision component based on an evaluation, such as anevaluation with a fuzzy logic rule. The measurement module 2226 mayassociate a measurement with each decision component, direct thegeneration of the measurement, and/or include information related to ameasurement.

An issue module 2228 may handle issuing an insurance policy based on theevaluation and measurements of one or more decision components and theapplication itself. According to an embodiment of the invention,decisions whether to ultimately issue an insurance policy or not toissue an insurance policy may be communicated to an applicant throughthe issue module 2228. The issue module 2228 may associate issuance ofan insurance policy with an applicant, with various measurement(s) andevaluation(s) of one or more policies and/or decision components andother information.

A retrieval module 2230 may be responsible for retrieving cases from acase database module 2240. According to an embodiment of the invention,queries submitted by a user for case-based reasoning may be coordinatedthrough the retrieval module 2230 for retrieving cases. Otherinformation and functions related for case retrieval may also beavailable.

A ranking module 2232 may be responsible for ranking cases retrievedbased on one or more queries received from a user. According to anembodiment of the invention, the ranking module 2232 may maintaininformation related to cases and associated with one or more queries.The ranking module 2232 may associate each case with the ranking(s)associated with one or more queries. Other information may also beassociated with the ranking module 2232.

A rate class module 2234 may handle various designations of rate classesfor one or more insurance policies. According to an embodiment of theinvention, each application may be assigned a rate class, where thepremiums paid by the applicant are based on the rate class. The rateclass module 2234 may associate a rate class with each insuranceapplication, and may assign a rate class based on evaluation andmeasurements of various applications and decision components, as well asbased on a decision by one or more underwriters. Other information mayalso be associated with the rate class module 2234.

Data may be stored in a data storage module 2236. The data storagemodule 2236 stores a plurality of digital files. According to anembodiment of the invention, a plurality of data storage modules 2236may be used and located on one or more data storage devices, where thedata storage devices are combined or separate from the controller module2212. One or more data storage modules 2236 may also be used to archiveinformation.

An adaptation module 2238 may be responsible for adapting the results ofone or more queries to determine which previous cases are most similarto the application for the present application for insurance. Otherinformation may also be associated with the adaptation module 2238.

All cases used in a case based reasoning may be stored in a casedatabase module 2240. According to an embodiment of the invention, aplurality of case database modules 2240 may be used and located on oneor more data storage devices, where the data storage devices arecombined or separate from the controller module 2212.

While the system 2200 of FIG. 22 discloses the requester device 2205connected to the network 2202, it should be understood that a personaldigital assistant (“PDA”), a mobile telephone, a television, or anotherdevice that permits access to the network 2202 may be used to arrive atthe system of the present invention.

According to another embodiment of the invention, a computer-usable andwriteable medium having a plurality of computer readable program codestored therein may be provided for practicing the process of the presentinvention. The process and system of the present invention may beimplemented within a variety of operating systems, such as a Windows®operating system, various versions of a Unix-based operating system(e.g., a Hewlett Packard, a Red Hat, or a Linux version of a Unix-basedoperating system), or various versions of an AS/400-based operatingsystem. For example, the computer-usable and writeable medium may becomprised of a CD ROM, a floppy disk, a hard disk, or any othercomputer-usable medium. One or more of the components of the system 2200may comprise computer readable program code in the form of functionalinstructions stored in the computer-usable medium such that when thecomputer-usable medium is installed on the system 2200, those componentscause the system 2200 to perform the functions described. The computerreadable program code for the present invention may also be bundled withother computer readable program software.

According to one embodiment, the central controller module 2212, thetransmitter module 2214, the receiver module 2216, the processor module2218, the decision component category module 2220, application categorymodule 2222, evaluation module 2224, measurement module 2226, issuemodule 2228, retrieval module 2230, ranking module 2232, rate classmodule 2234, data storage module 2236, adaptation module 2238, and casedatabase module 2240 may each comprise computer-readable code that, wheninstalled on a computer, performs the functions described above. Also,only some of the components may be provided in computer-readable code.

Additionally, various entities and combinations of entities may employ acomputer to implement the components performing the above-describedfunctions. According to an embodiment of the invention, the computer maybe a standard computer comprising an input device, an output device, aprocessor device, and a data storage device. According to otherembodiments of the invention, various components may be computers indifferent departments within the same corporation or entity. Othercomputer configurations may also be used. According to anotherembodiment of the invention, various components may be separate entitiessuch as corporations or limited liability companies. Other embodiments,in compliance with applicable laws and regulations, may also be used.

According to one specific embodiment of the present invention, thesystem may comprise components of a software system. The system mayoperate on a network and may be connected to other systems sharing acommon database. Other hardware arrangements may also be provided.

Other embodiments, uses and advantages of the present invention will beapparent to those skilled in the art from consideration of thespecification and practice of the invention disclosed herein. Thespecification and examples should be considered exemplary only. Theintended scope of the invention is only limited by the claims appendedhereto.

Information Summarization

The fuzzy rule-based decision engine and the case-based decision enginemay need to capture the medical/actuarial knowledge required to evaluateand underwrite an application. They may do so by using a rule set or acase base, respectively. However, both decision engines may also needaccess to all the relevant information that characterizes the newapplication. While the structured component of this information can becaptured as data and stored into a database (“DB”), the free-form natureof an attending physician statement (APS) may not be suitable toautomated parsing and interpretation. Therefore, for each applicationrequiring an APS, a summarization tool may be used that will convert allthe essential input variables from that statement into a structuredform, suitable for storage in a DB and for supporting automated decisionsystems. Furthermore, if the decision engines were not capable ofhandling this new application, then the use of the APS summarizationtool may be a productivity aid for a human underwriter, rather than anautomation tool.

The present invention may be used in connection with an engine toautomate decisions in business, commercial, or manufacturing processes.Such an engine may be based on (but not limited to) rules and/or cases.A process and system may be provided to structure and summarize keyinformation required by a reasoning system. According to an embodimentof the invention, summarized information required by a reasoning systemmay be used to underwrite insurance applications, and establish a rateclass corresponding to the perceived risk of the applicant. Such riskmay be characterized by several information sources, such as, but notlimited to, the application form, the APS, laboratory data, medicalinsurance consortium data bases, motor vehicle registration data bases,etc. Once this information has been gathered and compiled, theapplication risk may be evaluated by a human underwriter or by anautomated decision system. This evaluation is carried out leveraging themedical and actuarial knowledge of the human underwriter, which iscaptured in its essence by the automated reasoning system. According toan embodiment of the invention, an APS summarization tool may capturethe relevant variables that characterize a given medical impairment,allowing an automated reasoning system to determine the degree ofseverity of such impairment and to estimate the underlying insurancerisk.

According to an embodiment of the invention, a focus of this inventionon the individual medical impairments of a patient may provide 1)incremental deployment of the Automated Underwriting system as summariesfor new impairments can be developed and added; 2) efficient coverage,by addressing the most frequent impairments first, according to a Paretoanalysis of their frequencies; 3) efficient description of theimpairment, by including in the summary only the variables that couldhave an impact on the decision.

By way of example, an aspect of the present invention will be describedin terms of underwriting of an application for a fixed life insurancepolicy. Although the description focuses on the use of a reasoningsystem to automate the underwriting process of insurance policies, itwill be understood by one of ordinary skill in the art that theapplicability of this invention may be much broader, as it may apply toother reasoning system applications.

According to an embodiment of the invention, a method for executing andmanipulating an APS summarization tool may occur as illustrated in FIG.23. At step 2300, a summarizer with the appropriate medical knowledgewould log into a web-based system to begin the summarization process.According to an embodiment of the invention, the APS summarizationsystem may include a general form plus various condition specific forms,which are then filled out by the summarizer. The summarizer may firstfill out the general form, which contains data fields relevant to allapplicants. Condition specific forms are then filled out as needed, asthe summarizer discovers various features present in the APS beingsummarized.

At step 2302, a summarizer may verify that the APS corresponds to thecorrect applicant. This may be done by matching information on the APSitself with information about the applicant provided by the system. Byway of example, an applicant's name, date of birth, and social securitynumber could be matched. If a match is not made, the summarizer may notethis by checking the appropriate checkbox. According to an embodiment ofthe invention, at step 2304, failure to match an APS to an applicantwould end the summarizer's session for that applicant, and thesummarizer may recommend corrective action.

At step 2306, the general form is filled out. FIG. 24 illustrates ageneral form within a graphical user interface 2400 according to anembodiment of the invention. Graphical user interface 2400 may compriseaccess to any network browser, such as Netscape Navigator, MicrosoftExplorer, or others. Other means of accessing a network may also beused. Graphical user interface 2400 may include a control area 2402,whereby a summarizer may control various aspects of graphical userinterface 2400. Control may include moving to various portions of thenetwork via the graphical user interface 2400, printing information fromthe network, searching for information within the network, and otherfunctions used within a browser.

According to an embodiment of the invention, a general form 2400 mayprovide a fixed structure 2406 to capture the data within the system.According to an embodiment of the invention, different sections of theform may be organized into fields that are structured to provide only afixed set of choices for the summarizer. This may be done to standardizethe different pieces of information contained in the APS. By way ofexample, a fixed set of choices may be provided to a summarizer via apull-down menu 2408. For fields that cannot be treated as pull-downmenus (e.g., dates, numeric values of lab tests), such as entry field2410 labeled as “Initial date,” validation may be performed to ensurethat data entry errors are minimized, and to check that values arewithin allowable pre-determined limits. According to an embodiment ofthe invention, validation may include a “client-side” validation,designed to give the summarizer an immediate response if any of the datais incorrectly entered. A “client-side” validation may be achievedthrough JavaScript code embedded in the web pages. According to anembodiment of the invention, validation may include a “server-side”validation, which may be performed after data submission. “Server-side”validation may be designed primarily as a fail-safe check to preventerroneous data from entering the business-critical database.

According to an embodiment of the invention, link section 2404 mayprovide access to other portions of general form 2400. As illustrated inFIG. 24, link section 2404 may include links (such as hypertext links)to portions of general form 2400 that relate to blood pressure, familyhistory, nicotine use, build, lipids, alcohol use, cardiovascularfitness and tests, final check, comments, abnormal physical symptoms,abnormal blood results, abnormal urine results, abnormal pap test,mammogram, abnormal colonoscopy, chest x-ray, pulmonary function,substance abuse, and non-medical history. Other information within ageneral form 2400 may also be provided, and as such, may be linkedthrough link section 2404.

According to an embodiment of the invention, an APS summary maydistinguish between a blank data field and answers such as “don't know”or “not applicable,” thereby ensuring the completeness of the summary.For a general form submission, a final validation pass may be performedat step 2308 to alert the summarizer if certain required fields areblank. If required fields are blank, the system may require a summarizerto return to step 2306 and complete the general form. If the summarizerwishes to indicate that the particular piece of information is notknown, they may be required to specifically indicate so, therebymaintaining information about what information is specifically notknown. However, it will be recognized that not all fields willnecessarily require information. For example, certain fields may be“conditionally mandatory,” meaning that they require an answer only ifother fields have been filled out in a particular way. Use ofconditionally mandatory fields may ensure that all necessary informationis gathered. Further, ensuring that all required fields have been filledmay also ensure that the necessary information is gathered.

When the general form has been filled out and validated at step 2308,with all of the required fields entered, it may be necessary to completeone or more condition-specific forms. At step 2310, it is determined ifany condition-specific forms are required. If no condition specificforms are required, the results may be submitted to a database or otherstorage device for use at a later time at step 2320.

If a condition-specific form is required, a summarizer may select acondition-specific form to fill-in at step 2312. According to anembodiment of the invention, a summarizer may move from the general formto any of the condition-specific forms by following a hypertext linkembedded within the general form. By way of example, a link to acondition-specific form may be similar to, and/or same as links locatedwithin link portion 2404. Further, links to condition-specific forms maybe located within link portion 2404. A portion of the knowledge of whichcondition-specific forms are necessary may be obtained while filling outthe general form. In the current example of life insurance underwriting,these condition-specific forms may include hypertension, diabetes, etc.

FIG. 25 illustrates an example of a condition-specific form forhypertension within a graphical user interface 2500 according to anembodiment of the invention. Graphical user interface 2500 may compriseaccess to any network browser, such as Netscape Navigator, MicrosoftExplorer, or other browser. Other manners of accessing a network mayalso be used. Graphical user interface 2500 may include a control area2502, whereby a summarizer may control various aspects of graphic userinterface 2500. Control may include moving to various portions of thenetwork via the graphic user interface 2500, printing information fromthe network, searching for information within the network, and otherfunctions used within a browser.

Graphical user interface 2500 displays the hypertension-specific form,which may include various sections for inputting information related tohypertension. In the hypertension specific form illustrated in FIG. 25,initial identification section 2504 may enable a summarizer to provideinitial identification information, including whether an applicant hashypertension, the type of hypertension, whether it was secondaryhypertension, and if so, how the cause was removed or cured. Accordingto an embodiment of the invention, pull down menus may be used to ensurethat information entered is standardized for each patient. Otherinformation may also be gathered in initial identification section 2504.

EKG section 2506 may enable a summarizer to provide EKG information,including EKG readings within a specified time period (e.g., 6 months),chest X-rays within a specified time period (e.g., 6 months), and otherinformation related to EKG readings. According to an embodiment of theinvention, pull down menus may be used to ensure that informationentered is standardized for each patient. Patient cooperation section2508 may enable a summarizer to provide information related to apatient's cooperation, including whether the patient has cooperated,whether a patient's blood pressure is under control, and if so, for howmany months, and other information related to a patient's cooperation indealing with hypertension. According to an embodiment of the invention,pull down menus may be used to ensure that information entered isstandardized for each patient.

Blood pressure section 2510 may enable a summarizer to enter bloodpressure readings corresponding to various dates. According to anembodiment of the invention, separate entry fields may be provided forthe date the blood pressure reading was taken, (e.g., systolic reading(SBP) and the diastolic reading (DBP)). Other information may also beentered in blood pressure section 2510. Further, it will be understoodby those skilled in the art that other information related tohypertension may also be entered in a hypertension form displayed ongraphical user interface 2500.

At step 2314, a summarizer fills out a condition-specific form. For acondition-specific form, a final validation pass may be performed atstep 2316 to alert the summarizer if certain required fields are blank.If required fields are blank, the system may require a summarizer toreturn to step 2314 and complete the condition-specific form. As with ageneral form, if the summarizer wishes to indicate that the particularpiece of information is not known, they may be required to specificallyindicate so, thereby facilitating the tracking of what information isspecifically not known. However, it will be recognized that not allfields will necessarily require information. For example, certain fieldsmay be “conditionally mandatory,” meaning that they require an answeronly if other fields have been filled out in a particular way. Use ofconditionally mandatory fields may ensure that all necessary informationis gathered. Further, ensuring that all required fields have been filledmay also ensure that the necessary information is gathered.

If the condition-specific form has been filled out and validated at step2316, with all of the required fields entered, a summarizer maydetermine if additional condition-specific forms are necessary at step2318. If additional condition-specific forms are necessary, a summarizermay return to step 2312 and select the appropriate condition-specificform in which to enter information. If no additional condition-specificforms are required, the results may be submitted to a database or otherstorage device for use at a later time at step 2320.

Once the summarization is complete for a general form and any selectedcondition-specific forms, the summarizer may submit the results, such asdescribed in step 2320. The data may then be transferred over a network,such as the Internet, and stored in a database for later use. Accordingto an embodiment of the invention, different categorical data fields maybe presented to the summarizer as text, but for space efficiency areencoded as integer values in the database. A “translation table” to thecorresponding field meanings may then be provided as part of the designof the APS summary. The APS summarizer may provide a structured list oftopics, thereby enabling a trained person to summarize the mostsignificant information currently contained in a handwritten ortypewritten APS. Further, the APS summarizer may provide an efficientdescription of the data content of the APS. As stated above, the APSitself can be several tens of pages of doctor's notes. The APS summaryis designed to capture only the data fields that are relevant to theproblem at hand. In addition, a structured and organized description ofthe APS data may be provided. An APS itself can adhere to any arbitraryorder because of different doctor's styles. The APS summary may providea single consistent format for the data as required for an automatedsystem, and/or which facilitates the human underwriter's job greatly.

Since the APS summary may be captured in a database, the informationcontained in it may be easily available to any computer-basedapplication. Again, this is a requirement for an automated underwritingsystem, but it may provide many other advantages as well. For example,the APS data may otherwise be very difficult to analyze statistically,to categorize, or to classify. Since the APS summary forms can beweb-based, the physical location of the summarizers may be immaterial.The original APS sheets can be received in location X, scanned, sentover the Internet to location Y, where the APS summary is filled out,and the digital data from the summary can be submitted and stored on adatabase server in location Z. Further, the automated decision enginecan be in any fourth location, as could an individual running queriesagainst the APS summary database for statistical analysis or reportingpurposes.

According to an embodiment of the invention, general and conditionspecific forms may be written in HTML and JavaScript, which provide thevalidation functionality. A system for storing filled out summary datainto a remote database has also been created. This system was createdusing JavaBeans and JSP. Testing by experienced underwriters has beenperformed. The HTML summary forms are displayed to the underwriters viaa web browser, and the data from an actual APS is entered onto the form.The underwriter comments and feedback are captured on the form as well,and used to aid the continual improvement of the forms. In choosingwhich condition-specific forms to create, a statistical analysis wasdone of the frequencies of the various medical conditions. Theconditions that are most frequent were chosen to be worked on first. TheAPS summary does not have to cover all conditions before it is put intoproduction. Deployment of the APS summary may be progressive, coveringnew conditions one by one as new forms become available. Applicants withAPS requirements that are not covered in the current APS summary may beunderwritten using the usual procedures. Condition-specific forms maytherefore be added to the APS summary in order to increase coverage ofapplicants by the digital underwriting system.

Optimization of Fuzzy Rule-Based and Case-Based Decision Engines

According to an embodiment of the present invention, fuzzy rule-basedand case-based reasoning may be used to automate decisions in business,commercial, or manufacturing process. Specifically, a process and systemto automate the determination of optimal design parameters that impactthe quality of the output of the decision engines is described.

According to an embodiment of the invention, the optimization aspect mayprovide a structured and robust search and optimization methodology foridentifying and tuning the decision thresholds (cutoffs) of the fuzzyrules and internal parameters of the fuzzy rule-based decision engine(“RBE”), and the internal parameters of the case-based decision engine(“CBE”). These benefits may include a minimization of the degree of rateclass assignment mismatch between that of an expert human underwriterand automated rate class decisions. Further, the maintenance of theaccuracy of rule-based and case-based decision-making as decisionguidelines evolve with time may be achieved. In addition, identificationof ideal parameter combinations that govern the automateddecision-making process may occur.

The system and process of the present invention may apply to a class ofstochastic global search algorithms known as evolutionary algorithms toperform parameter identification and tuning. Such algorithms may beexecuted utilizing principles of natural evolution and may be robustadaptive search schemes suitable for searching non-linear,discontinuous, and high-dimensional spaces. Moreover, this tuningapproach may not require an explicit mathematical description of themulti-dimensional search space. Instead, this tuning approach may relysolely on an objective function that is capable of producing a relativemeasure of alternative solutions. According to an embodiment of theinvention, an evolutionary algorithm may be used for optimization withinan RBE and CBE. By way of example, an evolutionary algorithm (“EA”) mayinclude genetic algorithms, evolutionary programming, evolutionstrategies, and genetic programming. The principles of these relatedtechniques may define a general paradigm that is based on a simulationof natural evolution. EAs may perform their search by maintaining at anytime t a population P(t)={P₁(t), P₂(t), . . . , P_(p)(t)} ofindividuals. In this example, “genetic” operators that model simplifiedrules of biological evolution are applied to create the new anddesirably more superior population P(t+1). Such a process may continueuntil a sufficiently good population is achieved, or some othertermination condition is satisfied. Each P_(i)(t)εP(t), represents viaan internal data structure, a potential solution to the originalproblem. The choice of an appropriate data structure for representingsolutions may be more an “art” than a “science” due to the plurality ofdata structures suitable for a given problem. However, the choice of anappropriate representation may be a critical step in a successfulapplication of EAs. Effort may be required to select a data structurethat is compact, minimally superfluous, and can avoid creation ofinfeasible individuals. For instance, if the problem domain requiresfinding an optimal real vector from the space defined by dissimilarlybounded real coordinates, it may be more appropriate to choose as arepresentation a real-set-array (e.g., bounded sets of real numbers)instead of a representation capable of generating bit strings. Arepresentation that generates bit strings may create many infeasibleindividuals, and can be certainly longer than a more compact sequence ofreal numbers. Closely linked to a choice of representation of solutionsmay be a choice of a fitness function ψ: P(t)→R, that assigns credit tocandidate solutions. Individuals in a population are assigned fitnessvalues according to some evaluation criterion. Fitness values maymeasure how well individuals represent solutions to the problem. Highlyfit individuals are more likely to create offspring by recombination ormutation operations. Weak individuals are less likely to be picked forreproduction, so they eventually die out. A mutation operator introducesgenetic variations in the population by randomly modifying some of thebuilding blocks of individuals. Evolutionary algorithms are essentiallyparallel by design, and at each evolutionary step a breadth search ofincreasingly optimal sub-regions of the options space is performed.Evolutionary search is a powerful technique of solving problems, and isapplicable to a wide variety of practical problems that are nearlyintractable with other conventional optimization techniques. Practicalevolutionary search schemes do not guarantee convergence to the globaloptimum in a predetermined finite time, but they are often capable offinding very good and consistent approximate solutions. However, theyare shown to asymptotically converge under mild conditions.

An evolutionary algorithm may be used within a process and system forautomating the tuning and maintenance of fuzzy rule-based and case-baseddecision systems used for automated decisions in insurance underwriting.While this approach is demonstrated for insurance underwriting, it isbroadly applicable to diverse rule-based and case-based decision-makingapplications in business, commercial, and manufacturing processes.Specifically, we describe a structured and robust search andoptimization methodology based on a configurable multi-stageevolutionary algorithm for identifying and tuning the decisionthresholds of the fuzzy rules and internal parameters of the fuzzyrule-based decision engine and the internal parameters of the case-baseddecision engine. The parameters of the decision systems impact thequality of the decision-making, and are therefore critical. Furthermore,this tuning methodology can be used periodically to update and maintainthe decision engines.

As stated above, these fuzzy logic systems may have many parameters thatcan be freely chosen. These parameters may either be fit to reproduce agiven set of decisions, or set by management in order to achieve certainresults, or a combination of the two. A large set of cases may beprovided by the company as a “certified case base.” According to anembodiment of the invention, the statistics of the certified case basemay closely match the statistics of insurance applications received in areasonable time window. According to an embodiment of the invention,there will be many more cases than free parameters, so that the systemwill be over-determined. Then, an optimal solution may be found whichminimizes the classification error between a decision engine's outputand the supplied cases. When considering maintenance of a system, it maybe convenient and advantageous that the parameters are chosen usingoptimization vs. a set of certified cases. New fuzzy rules and certifiedcases may be added, or aggregation rules may change. The fuzzy logicsystems may be kept current, allowing the insurance company to implementchanges quickly and with zero variability.

The parameter identification and tuning problem which may presented inthis invention can be mathematically described as a minimizationproblem: min

$\min\limits_{x \in \chi}\;{\psi(x)}$where χ=χ₁×χ₂× . . . × χ_(n) χ_(i)⊂

and ψ:χ→

₊where χ is an n-dimensional bounded hyper-volume (parametric searchspace) in the n-dimensional space of reals, x is a parameter vector, andψ is the objective function that maps the parametric search space to thenon-negative real line.

FIG. 26 illustrates such a minimization (optimization) problem accordingto an embodiment of the invention in the context of the applicationdomain, where the search space χ corresponds to the space of decisionengine designs induced by the parameters imbedded in the decisionengine, and the objective function ψ measures the corresponding degreeof rate-class assignment mismatch between that of the expert humanunderwriter and the decision-engine for the certified case base. Anevolutionary algorithm iteratively generates trial solutions (trialparameter vectors in the space χ), and uses their correspondingconsequent degree of rate-class assignment mismatch as the searchfeedback. Thus, at step 2602, a space of decision engine's designs isprobed. At step 2604, a mismatch matrix, which will be described ingreater detail below, is generated based on the rate-class decisionsgenerated for the cases by the decision engine. Penalties formismatching cases are assigned at step 2606. The evolutionary algorithmuses the corresponding degree of rate-class assignment mismatches, andthe associated penalties to provide feedback to the decision engine atstep 2608. The system may then refine the internal parameters anddecision thresholds in the decision engine at step 2602, and proceedthrough the process again. Thus, an iterative process may be performed.

FIG. 27 illustrates an example of an encoded population maintained bythe evolutionary algorithm at a given generation. According to anembodiment of the invention, each individual in the population is atrial vector of design parameters representing fuzzy rule thresholds andinternal parameters of the decision engine. Each percentage entry mayrepresent a value of a trial parameter that falls within a correspondingbounded real line. Each trial solution vector may be used to initializean instance of the decision engine, following which each of the cases inthe certified case base is evaluated.

FIG. 28 illustrates a process schematic for an evaluation systemaccording to an embodiment of the invention. Trial design parameters areprovided at an input module 2802. The trial design parameters areautomatically input to decision engine 2804. Case subset 2808 fromcertified case base 2806 is input into decision engine 2804. Certifiedcase base 2806 may comprises cases that have been certified as beingcorrect. Case subset 2808 may be a predetermined number of cases fromcertified case base 2806. According to an embodiment of the invention,case subset 2808 may comprise two thousand (2000) certified cases.According to an embodiment of the invention, case subset 2808 maycomprise a number of times the number of tunable parameters of decisionengine 2804. The cases within case subset 2808 are processed in decisionengine 2804, and output to decision engine case decisions 2810.

Once all the cases in the certified case base are evaluated, a squareconfusion matrix 2814 is created. According to an embodiment of theinvention, confusion matrix 2814 may be generated by comparing decisionengine case decisions 2810 and certified case decisions 2812. The rowsof confusion matrix 2814 may correspond to certified case decisions 2812as determined by an expert human underwriter, and the columns ofconfusion matrix 2814 may correspond to the decision engine casedecisions 2810 for the cases in the certified case base. By way ofexample, assume a case has been assigned a category S from certifiedcase decision 2812 (from the matrix 2814) and a category PB fromdecision engine decision 2810. Under these categorizations, the casewould count towards an entry in the cell at row 3 and column 1. In thisexample, the certified case decision 2812 places the case in a higherrisk category, while the decision engine case decision 2810 places thecase in a lower risk category. Therefore, for this particular case, thedecision engine 2810 has been more liberal in decision-making. By way ofanother example, if on the other hand both the certified case decision2812 and the decision engine case decisions 2810 agree as uponcategorizing the case in class S, then the case would count towards anentry in the cell at row 3 and column 3. By way of another example, ifthe certified case decision 2812 is PB, but the machine decision 2810 isS, then clearly the machine decision is more strict.

According to an embodiment of the invention, it may be desirable to usea decision engine that is able to place the maximum number of certifiedcases along the main diagonal of confusion matrix 2814. It may also bedesirable to determine those parameters 2802 for decision engine 2804that produce such results (e.g., minimize the degree of rate classassignment confusion or mismatch between certified case decisions 2812and decision engine case decisions 2810). Confusion matrix 2814 may beused as the foundation to compute an aggregate mismatch penalty orscore, using penalty module 2816. According to an embodiment of theinvention, a penalty matrix may be derived from actuarial studies and iselement-by-element multiplied with the cells of the confusion matrix2814 to generate an aggregate penalty/score for a trial vector ofparameters in the evolutionary search. A summation over the number ofrows and columns of the matrix may occur, and that should now be “T”(upper case T), as the confusion matrix M may be of a dimension T×T.Other process systems may also be used to achieve the present invention.

According to an embodiment of the invention, an evolutionary algorithmmay utilize only the selection and stochastic variation (mutation)operations to evolve generations of trial solutions. While the selectionoperation may seek to exploit known search space regions, the mutationoperation may seek to explore new regions of the search space. Such analgorithm is known to those of ordinary skill in the art. One example ofthe theoretical foundation for such an algorithm class appears inModeling and Convergence Analysis of Distributed CoevolutionaryAlgorithms, Raj Subbu and Arthur C. Sanderson, Proceedings of the IEEEInternational Congress on Evolutionary Computation, 2000.

FIG. 29 illustrates an example of the mechanics of such an evolutionaryprocess. At step 2902, an initial population of trial decision engineparameters is created. Proportional selection occurs at step 2904 and anintermediate population is created at step 2906. Stochastic variationoccurs at step 2908, and a new population is created at step 2910. Thenew population may then be subject to proportional selection at step2904, thereby creating an iterative process.

According to an embodiment of the invention, the evolutionary algorithmmay use a specified fixed population size and operate in one or morestages, each stage of which may be user configurable. A stage isspecified by a tuple consisting of a fixed number of generations andnormalized spread of a Gaussian distribution governing randomizedsampling. A given solution (also called the parent) in generation i maybe improved by cloning it to create two identical child solutions fromthe parent solution.

The first child solution may be mutated according to a uniformdistribution within the allowable search bounds. The second childsolution may then be mutated according to the Gaussian distribution forgeneration i. If the mutated solution falls outside of the allowablesearch bounds, then the sampling is repeated a few times until anacceptable sample is found. If no acceptable sample is found within theallotted number of trials, then the second child solution may be mutatedaccording to a uniform distribution. The best of the parent and twochild solutions is retained and is transferred to the population atgeneration i+1. In addition, it is ensured via elitism that theimprovement in the best performing individual of each generation ofevolution i+n (where n is an increasing whole number) is a monotonefunction. According to an embodiment of the invention, the process maybe repeated until i+n generation has been generated, where i+n is awhole number.

While the invention has been particularly shown and described within theframework of an insurance underwriting application, it will beappreciated that variations and modifications can be effected by aperson of ordinary skill in the art without departing from the scope ofthe invention. For example, one of ordinary skill in the art willrecognize that the fuzzy rule-based or case-based engine of thisinvention can be applied to any other transaction-oriented process inwhich underlying risk estimation is required to determine the pricestructure (premium, price, commission, etc.) of an offered product, suchas insurance, re-insurance, annuities, etc. Furthermore, thedetermination of the confidence factor and the optimization of thedecision engines transcend the scope of insurance underwriting. Aconfidence factor obtained in the manner described in this documentcould be determined from any application of a case-based reasoner(whether it is fuzzy or not). Similarly, the engine optimization processdescribed in this document can be applied to any engine in which thestructure of the engine has been defined and the parametric values ofthe engine need to be specified to meet a predefined performance metric.Furthermore, one of ordinary skill in the art will recognize that suchdecision engines do not need to be restricted to insurance underwritingapplications.

1. A system that determines a confidence factor for an insuranceapplication underwriting decision based on a comparison of at least oneprevious underwritten insurance application, the system implemented on acomputer processing system including a computer readable medium, thesystem comprising: an identification application, tangibly embodied inthe computer readable medium, that identifies a plurality of internalparameters that are identified as potentially affecting the probabilityof misclassification of a risk category of the insurance application; anestimation module, tangibly embodied in the computer readable medium,that estimates the conditional probability of misclassification of arisk category for each of the identified parameters; a translationmodule, tangibly embodied in the computer readable medium, thattranslates the conditional probability of misclassification of a riskcategory into a soft constraint for each of the identified parameters;and a definition module, tangibly embodied in the computer readablemedium, to determine a confidence factor based on the plurality of softconstraints, the confidence factor pertaining to the insuranceapplication underwriting decision; and the system comparing theconfidence factor to a confidence threshold to determine a confidencelevel in the insurance application underwriting decision.
 2. The systemaccording to claim 1, where at least one parameter comprises at leastone of: a) the number of retrieved cases; b) the variability of theretrieved cases; c) the number of retrieved cases thresholded bysimilarity value; d) the variability of retrieved cases thresholded bysimilarity value e) the number of refined cases; f) the variability ofrefined cases g) the number of refined cases, thresholded by similarityvalue h) the variability of refined cases thresholded by similarityvalue i) the measure of strength of mode; j) the number of retrievedcases weighted by similarities; k) the variability of retrieved casesweighted by similarities; l) the number of refined cases weighted bysimilarities; and m) the variability of refined cases weighted forsimilarities.
 3. The system according to claim 1, the system furthercomprises: an access module that accesses a case base containing aplurality of cases whose associated decisions have been certifiedcorrect; a selector that selects one of the plurality of cases, wherethe selected case is defined as the probe case; an identification modulethat identifies at least one internal parameter of the probe case thatmay affect the probability of misclassification of a risk category; adetermination module that determines the underwriting classification ofthe probe case based on the remaining plurality of cases within the casebase; a comparison module that compares the underwriting classificationdetermination with the original certified decision of the probe case;and a recorder that records the comparison and the identified internalparameters for the probe case.
 4. The system according to claim 1,wherein the translation module that translates the conditionalprobability of misclassification of a risk category into a softconstraint for each of the parameters further comprises a penalty formisclassification of a risk category and a reward for correctclassification.
 5. The system according to claim 1, wherein thetranslation module that translates the conditional probability ofmisclassification of a risk category into a soft constraint for each ofthe parameters further comprises an aggregating functions using aweighted sum of rewards and penalties with increasing penalties formisclassifications of a risk category.
 6. The system according to claim1, further comprising code for generating a soft constraint evaluationvector that contains the degree to which each of the parameterssatisfies its corresponding soft constraint.
 7. The system according toclaim 6, further comprising code that computes a confidence factor basedon the intersection of all the soft constraints evaluations contained inthe soft constraint evaluation vector.
 8. The system according to claim1, wherein the insurance underwriting decision is an automated decision.9. A system that determines the confidence factor for an applicationdecision based on a comparison of at least one previous applicationdecision, the system implemented on a computer processing systemincluding a computer readable medium, the system comprising: anidentification module, tangibly embodied in the computer readablemedium, that identifies a plurality of internal parameters that areidentified as potentially affecting the probability of misclassificationof a risk category of the insurance application; an estimation module,tangibly embodied in the computer readable medium, to estimate theconditional probability of misclassification of a risk category for eachof the identified parameters; a translation module, tangibly embodied inthe computer readable medium, that translates the conditionalprobability of misclassification of a risk category into a softconstraint for each of the parameters; and a determination module,tangibly embodied in the computer readable medium, for defining arun-time function to evaluate a confidence threshold based on the softconstraint.
 10. The system according to claim 9, wherein the estimatingthe conditional probability of misclassification of a risk category foreach of the identified parameters further comprises: an access modulefor accessing a case base containing a plurality of cases whoseassociated decisions were certified correct; a selector for selectingone of the plurality of cases, where the selected case is defined as theprobe case; an identification module that identifies a plurality ofinternal parameters that are identified as potentially affecting theprobability of misclassification of a risk category; a determinationmodule that determines the underwriting classification of the probe casebased on the remaining plurality of cases within the case base; acomparison module for comparing the underwriting classificationdetermination with the original certified decision of the probe case;and a recorder for recording the comparison and the at least oneidentified internal parameter for the probe case.
 11. The systemaccording to claim 9, wherein translating the conditional probability ofmisclassification of a risk category into a soft constraint for eachparameter further comprises a penalty for misclassification of a riskcategory and a reward for correct classification.
 12. The systemaccording to claim 9, wherein translating the conditional probability ofmisclassification of a risk category into a soft constraint for eachparameter further comprises an aggregating functions using a weightedsum of rewards and penalties with increasing penalties formisclassifications of a risk category.
 13. The system according to claim9, further comprising a generation for generating a soft constraintevaluation vector that contains the degree to which each of theparameters satisfies its corresponding soft constraint.
 14. The systemaccording to claim 9, further comprising a computation module forcomputing a confidence factor based on the intersection of all the softconstraints evaluations contained in the soft constraint evaluationvector.
 15. The system according to claim 9, wherein the applicationdecision is an automated application decision.
 16. A system thatdetermines the confidence factor for an automated insurance applicationunderwriting decision based on a comparison of at least one previousunderwritten insurance application, the system implemented on a computerprocessing system including a computer readable medium, the systemcomprising: an identification module, tangibly embodied in the computerreadable medium, that identifies a plurality of internal parameters thatare identified as potentially affecting the probability ofmisclassification of a risk category of the insurance application; anestimating module, tangibly embodied in the computer readable medium,that estimates the conditional probability of misclassification of arisk category for each of the identified parameters, including thesub-components of: a) an access module that accesses a case basecontaining a plurality of cases whose associated decisions have beencertified correct; b) a selector that selects one of the plurality ofcases, where the selected case is defined as the probe case; c) anidentification module that identifies at least one internal parameter ofthe probe case that may affect the probability of misclassification of arisk category; d) a determination module that determines theunderwriting classification of the probe case based on the remainingplurality of cases within the case base; e) a comparison module thatcompares the underwriting classification determination with the originalcertified decision of the probe case; and f) a recorder that records thecomparison and at least one identified internal parameter for the probecase; a translation module, tangibly embodied in the computer readablemedium, that translates the conditional probability of misclassificationof a risk category into a soft constraint for each of the parameters;and a definition module, tangibly embodied in the computer readablemedium, that defines a run-time function to evaluate a confidencethreshold based on the soft constraint.
 17. A system that determines theconfidence factor for an automated insurance application underwritingdecision based on a comparison of at least one previous underwritteninsurance application, the system implemented on a computer processingsystem including a computer readable medium, the system comprising: anidentification module, tangibly embodied in the computer readablemedium, that identifies a plurality of internal parameters that areidentified as potentially affecting the probability of misclassificationof a risk category of the insurance application; an estimation module,tangibly embodied in the computer readable medium, that estimates theconditional probability of misclassification of a risk category for eachof the identified parameters; a translation module tangibly embodied inthe computer readable medium, that translates the conditionalprobability of misclassification of a risk category into a softconstraint for each of the parameters through an aggregating functionusing a weighted sum of rewards and penalties with increasing penaltiesfor misclassifications of a risk category; and a definition module,tangibly embodied in the computer readable medium, defining a run-timefunction to evaluate the confidence based on the soft constraint.
 18. Asystem that determines the confidence factor for an automated insuranceapplication underwriting decision based on a comparison of at least oneprevious underwritten insurance application, the system implemented on acomputer processing system including a computer readable medium, thesystem comprising: an identification module, tangibly embodied in thecomputer readable medium, that identifies a plurality of internalparameters that are identified as potentially affecting the probabilityof misclassification of a risk category of the insurance application; anestimation module, tangibly embodied in the computer readable medium,that estimates the conditional probability of misclassification of arisk category for each of the identified parameter; a translationmodule, tangibly embodied in the computer readable medium, thattranslates the conditional probability of misclassification of a riskcategory into a soft constraint for each of the parameters; a definitionmodule, tangibly embodied in the computer readable medium, that definesa run-time function to evaluate a confidence threshold based on the softconstraint; and a generator that generates a soft constraint evaluationvector that contains the degree to which each of the parameterssatisfies its corresponding soft constraint.
 19. The system according toclaim 18, further comprising a computation module that computes aconfidence factor based on the intersection of all the soft constraintsevaluations contained in the soft constraint evaluation vector.